Development of the Verbal Autopsy Instrument for COVID-19 (VAIC).
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ABSTRACT: Background
Improving accuracy of identification of COVID-19-related deaths is essential to public health surveillance and research. The verbal autopsy, an established strategy involving an interview with a decedent's caregiver or witness using a semi-structured questionnaire, may improve accurate counting of COVID-19-related deaths.Objective
To develop and pilot-test the Verbal Autopsy Instrument for COVID-19 (VAIC) and a death adjudication protocol using it.Methods/key results
We used a multi-step process to design the VAIC and a protocol for its use. We developed a preliminary version of a verbal autopsy instrument specifically for COVID. We then pilot-tested this instrument by interviewing respondents about the deaths of 15 adults aged ≥65 during the initial COVID-19 surge in New York City. We modified it after the first 5 interviews. We then reviewed the VAIC and clinical information for the 15 deaths and developed a death adjudication process/algorithm to determine whether the underlying cause of death was definitely (40% of these pilot cases), probably (33%), possibly (13%), or unlikely/definitely not (13%) COVID-19-related. We noted differences between the adjudicated cause of death and a death certificate.Conclusions
The VAIC and a death adjudication protocol using it may improve accuracy in identifying COVID-19-related deaths.
SUBMITTER: Rosen T
PROVIDER: S-EPMC8231744 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Verbal autopsy (VA) is a practical method for determining probable causes of death at the population level in places where systems for medical certification of cause of death are weak. VA methods suitable for use in routine settings, such as civil registration and vital statistics (CRVS) systems, have developed rapidly in the last decade. These developments have been part of a growing global momentum to strengthen CRVS systems in low-income countries. With this momentum have come pressure for continued research and development of VA methods and the need for a single standard VA instrument on which multiple automated diagnostic methods can be developed. METHODS AND FINDINGS:In 2016, partners harmonized a WHO VA standard instrument that fully incorporates the indicators necessary to run currently available automated diagnostic algorithms. The WHO 2016 VA instrument, together with validated approaches to analyzing VA data, offers countries solutions to improving information about patterns of cause-specific mortality. This VA instrument offers the opportunity to harmonize the automated diagnostic algorithms in the future. CONCLUSIONS:Despite all improvements in design and technology, VA is only recommended where medical certification of cause of death is not possible. The method can nevertheless provide sufficient information to guide public health priorities in communities in which physician certification of deaths is largely unavailable. The WHO 2016 VA instrument, together with validated approaches to analyzing VA data, offers countries solutions to improving information about patterns of cause-specific mortality.
| S-EPMC5761828 | biostudies-literature
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.
| S-EPMC3912496 | biostudies-literature
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.
| S-EPMC3160942 | biostudies-literature
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.
| S-EPMC10795603 | biostudies-literature
Project description:BackgroundNigeria has one of the highest under-five mortality rates in the world. Identifying the causes of these deaths is crucial to inform changes in policy documents, design and implementation of appropriate interventions to reduce these deaths. This study aimed to provide national and zonal-level estimates of the causes of under-five death in Nigeria in the 2013-2018 periods.MethodsWe conducted retrospective inquiries into the cause of deaths of 948 neonates and 2,127 children aged 1-59 months as identified in the 2018 Nigeria Demographic and Health Survey (NDHS). The verbal autopsy asked about signs and symptoms during the final illness. The Physician Coded Verbal Autopsy (PCVA) and Expert Algorithm Verbal Autopsy (EAVA) methods were employed to assign the immediate and underlying cause of deaths to all cases.ResultFor the analysis, sampling weights were applied to accommodate non-proportional allocation. Boys accounted for 56 percent of neonatal deaths and 51.5 percent of the 1-59-months old deaths. About one-quarter of under-5 mortality was attributed to neonatal deaths, and 50 percent of these neonatal deaths were recorded within 48 h of delivery. Overall, 84 percent of the under-5 deaths were in the northern geopolitical zones. Based on the two methods for case analysis, neonatal infections (sepsis, pneumonia, and meningitis) were responsible for 44 percent of the neonatal deaths, followed by intrapartum injury (PCVA: 21 percent vs. EAVA: 29 percent). The three main causes of death in children aged 1-59 months were malaria (PCVA: 23 percent vs. EAVA: 35 percent), diarrhoea (PCVA: 17 percent vs. EAVA: 23 percent), and pneumonia (PCVA: 10 percent vs. EAVA: 12 percent). In the North West, where the majority of under-5 (1-59 months) deaths were recorded, diarrhoea was the main cause of death (PCVA: 24.3 percent vs. EAVA: 30 percent).ConclusionThe causes of neonatal and children aged 1-59 months deaths vary across the northern and southern regions. By homing on the specific causes of mortality by region, the study provides crucial information that may be useful in planning appropriately tailored interventions to significantly reduce under-five deaths in Nigeria.
| S-EPMC9172014 | biostudies-literature
Project description:BackgroundA verbal autopsy (VA) is a post-hoc written interview report of the symptoms preceding a person's death in cases where no official cause of death (CoD) was determined by a physician. Current leading automated VA coding methods primarily use structured data from VAs to assign a CoD category. We present a method to automatically determine CoD categories from VA free-text narratives alone.MethodsAfter preprocessing and spelling correction, our method extracts word frequency counts from the narratives and uses them as input to four different machine learning classifiers: naïve Bayes, random forest, support vector machines, and a neural network.ResultsFor individual CoD classification, our best classifier achieves a sensitivity of.770 for adult deaths for 15 CoD categories (as compared to the current best reported sensitivity of.57), and.662 with 48 WHO categories. When predicting the CoD distribution at the population level, our best classifier achieves.962 cause-specific mortality fraction accuracy for 15 categories and.908 for 48 categories, which is on par with leading CoD distribution estimation methods.ConclusionsOur narrative-based machine learning classifier performs as well as classifiers based on structured data at the individual level. Moreover, our method demonstrates that VA narratives provide important information that can be used by a machine learning system for automated CoD classification. Unlike the structured questionnaire-based methods, this method can be applied to any verbal autopsy dataset, regardless of the collection process or country of origin.
| S-EPMC6617656 | biostudies-literature
Project description:BackgroundVerbal autopsy (VA) has emerged as an increasingly popular technique to assign cause of death in parts of the world where the majority of deaths occur without proper medical certification. The purpose of this study was to examine the key characteristics of studies that have attempted to validate VA cause of death against an established cause of death.MethodsA systematic review was conducted by searching the MEDLINE, EMBASE, Cochrane-library, and Scopus electronic databases. Included studies contained 1) a VA component, 2) a validation component, and 3) original analysis or re-analysis. Characteristics of VA studies were extracted. A total of 527 studies were assessed, and 481 studies screened to give 66 studies selected for data extraction.ResultsSixty-six studies were included from multiple countries. Ten studies used an existing database. Sixteen studies used the World Health Organization VA questionnaire and 5 studies used the Population Health Metrics Research Consortium VA questionnaire. Physician certification was used in 36 studies and computer coded methods were used in 14 studies. Thirty-seven studies used high level comparator data with detailed laboratory investigations.ConclusionMost studies found VA to be an effective cause of death assignment method and compared VA cause of death to a high-quality established cause of death. Nonetheless, there were inconsistencies in the methodologies of the validation studies, and many used poor quality comparison cause of death data. Future VA validation studies should adhere to consistent methodological criteria so that policymakers can easily interpret the findings to select the most appropriate VA method.Prospero registrationCRD42020186886.
| S-EPMC9706899 | biostudies-literature
Project description:Severe COVID-19 disease caused by SARS-CoV-2 is frequently accompanied by dysfunction of the lungs and extrapulmonary organs. However, the organotropism of SARS-CoV-2 and the port of virus entry for systemic dissemination remain largely unknown. We profiled 26 COVID-19 autopsy cases from four cohorts in Wuhan, China, and determined the systemic distribution of SARS-CoV-2. SARS-CoV-2 was detected in the lungs and multiple extrapulmonary organs of critically ill COVID-19 patients up to 67 days after symptom onset. Based on organotropism and pathological features of the patients, COVID-19 was divided into viral intrapulmonary and systemic subtypes. In patients with systemic viral distribution, SARS-CoV-2 was detected in monocytes, macrophages, and vascular endothelia at blood-air barrier, blood-testis barrier, and filtration barrier. Critically ill patients with long disease duration showed decreased pulmonary cell proliferation, reduced viral RNA, and marked fibrosis in the lungs. Permanent SARS-CoV-2 presence and tissue injuries in the lungs and extrapulmonary organs suggest direct viral invasion as a mechanism of pathogenicity in critically ill patients. SARS-CoV-2 may hijack monocytes, macrophages, and vascular endothelia at physiological barriers as the ports of entry for systemic dissemination. Our study thus delineates systemic pathological features of SARS-CoV-2 infection, which sheds light on the development of novel COVID-19 treatment.
| S-EPMC8208380 | biostudies-literature
Project description:A 93-year-old woman was admitted with a 10-day history of cough and prostration. Thoracic computed tomography revealed extensive ground-glass opacities in both the lungs. The polymerase chain reaction test of sputum for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) was positive. She was treated with antiviral agents and steroid pulse therapy. However, her oxygen saturation gradually declined, and she died 10 days after hospitalization. The most important autopsy finding was fuzzily segmented diffuse alveolar damage (DAD) that expanded from the subpleural to the medial area. No remarkable changes were observed in organs other than the lungs. Therefore, pneumocytes were suggested as the primary target for SARS-CoV-2, which might explain why coronavirus infectious disease-19 is a serious condition. Thus, early treatment is essential to prevent viral replication from reaching a level that triggers DAD.
| S-EPMC7436745 | biostudies-literature
Project description:BackgroundReports of mucormycosis, an infectious disease that commonly affects immunocompromised individuals, have increased during the ongoing coronavirus disease 2019 (COVID-19) pandemic. Disseminated mucormycosis associated with COVID-19 is rare but fatal and is characterized by an aggressive clinical course and delayed diagnosis. Our report documents a case of disseminated mucormycosis after COVID-19 infection. This is a rare pathological autopsy report on COVID-19-associated mucormycosis.Case summaryA 58-year-old man was transferred to our hospital with severe COVID-19 pneumonia. During treatment for acute respiratory distress syndrome, he developed intra-abdominal bleeding that required a right hemicolectomy and ileostomy for hemostasis. The ileostoma and surgical wound developed necrosis followed by sepsis and multi-organ failure, which led to death. An autopsy revealed multiple thrombi associated with Rhizopus oryzae infection, which led to the necrosis of multiple infected organs.ConclusionEarly suspicion and diagnosis followed by treatment are keys to better outcomes of mucormycosis in patients with severe COVID-19.
| S-EPMC9561594 | biostudies-literature