Can verbal autopsy improve the diagnostic accuracy of Physicians in Shanghai? Application of SmartVA

Abstract Background Accurate data on causes of death are essential for policy makers and public health experts to plan appropriate health policies and interventions to improve population health. Whereas approximately 30% deaths of Shanghai either occur at home or are not medically attended; the recorded cause of death in these cases may be less reliable than for a hospital death. Verbal Autopsy is a practical method that can help determine causes of death in regions where medical records are insufficient or unavailable. In this research, the smart VA tool was adopted to assign the cause of death of home deaths and to validate the accuracy and efficiency of the tool, the results were compared with routine practice to ascertain the value, if any, of incorporating VA into the diagnostic practices of physician in Shanghai certifying the cause of home deaths. Methods This pilot study selected home deaths certified by 16 community health centers from 3 districts represent urban, suburb, and urban-suburb areas in Shanghai, from December 2017 to June 2018. The medical records for all deaths for which a VA was carried out in these 3 districts during same period were carefully evaluated an independent Medical Record Review (MRR) team. Causes of death from both the SmartVA sample and the UCOD from the MRR were transformed to the SmartVA cause list for comparison. The concordance between the initial diagnosis and MRR UCOD and post-VA diagnosis and MRR UCOD was assessed using Chance Corrected Concordance. Results Overall CSMF accuracy improved from 0.93, based on the initial diagnosis, to 0.96 after the application of SmartVA. The misclassification of the initial diagnosis compared to that from the MRR. 86.3% of the initial diagnoses assigned the correct CODs, after the VA investigation, 90.5% of the post-VA diagnosis assigned the correct CODs. Conclusions Although Shanghai has an established and well-functioning CRVS system, SmartVA for Physicians contributed to an improvement in the accuracy of death certification. In addition, SmartVA may be a useful tool for inferring some special causes of death, such as those CODs classified as undetermined.

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Assessing the quality of evidence for verbal autopsy diagnosis of stroke in Vietnam

Journal of Neurosciences in Rural Practice ◽

10.4103/0976-3147.102603 ◽

2012 ◽

Vol 03 (03) ◽

pp. 267-275 ◽

Cited By ~ 6

Author(s):

Sachin Gupta ◽

Thi Quynh Trang Khieu ◽

Chalapati Rao ◽

Ngo Anh ◽

Nguyen Phuong Hoa

Keyword(s):

Developing Countries ◽

Hospital Admission ◽

Cause Of Death ◽

Verbal Autopsy ◽

Validation Studies ◽

Medical Records ◽

Causes Of Death ◽

Cerebrovascular Diseases ◽

Clinical Records

ABSTRACT Background: Information on the leading causes of mortality will continue to rely on verbal autopsy (VA) in developing countries. The accuracy of VA methods in correctly ascertaining the cause for each individual death is crucial in order to have confidence in the data collected through the procedure. Accuracy of the VA procedure is generally established by carrying out validation studies involving a comparison of the underlying cause of death derived from the VA with a reference underlying cause from medical records. Such validation is only possible in cases for which clinical records are available, and this is clearly not the case for most deaths in developing countries. We attempt to verify the accuracy of VA evidence by reviewing the responses to specific symptom questions and other information recorded in verbal autopsy questionnaires that were assigned cerebrovascular conditions (stroke) as causes of death upon physician review in Vietnam. Materials and Methods: A national sample mortality surveillance activity identified deaths and causes of death that had occurred during 2008 in selected communes in 16 provinces distributed across Vietnam. All cases from the northern provinces of Hanoi, Hai Duong, Quang Ninh and Thanh Hoa with ICD codes pertaining to cerebrovascular diseases were identified. A total of 326 VA questionnaires for deaths from cerebrovascular diseases were reviewed and analysed in detail for the presence of symptoms pertaining to stroke. The respondents’ narration of the chronological disease history and the hospital diagnosis was also examined with an aim to explore supporting signs for diagnosis and to verify the quality of VA interview. Differences between responses among cases with and without hospital admission were examined using Chi-squared test of statistical significance. Results: Ninty percent of the cases diagnosed as stroke were found to have positive response to the key symptoms; viz., paralysis (in structured question or free text) and history of stroke. For the remaining 10% of cases, stroke was assigned as a cause-of-death based on other suggestive cardiac signs and symptoms such as hypertension, unconsciousness, or headache, etc. Community had different perspectives of “paralysis” and “stroke” which might have affected the diagnosis of stroke in some aspects. Respondents of cases with hospital admission or visit were found to have a better recall of disease symptoms than those without hospital admission. Conclusion: The results of this study suggest the possible utility of VA content analysis method to back up the low coverage of conventional validation studies in developing countries owing to nonavailability of medical records. The understanding of the VA content would also form the basis for improvement in the quality of interviews and collection of data to achieve better quality information in future.

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Postmortem examination protocol and systematic re-evaluation reduce the proportion of unexplained stillbirths

Journal of Perinatal Medicine ◽

10.1515/jpm-2019-0426 ◽

2020 ◽

Vol 48 (8) ◽

pp. 771-777

Author(s):

Maria Pekkola ◽

Minna Tikkanen ◽

Mikko Loukovaara ◽

Jouko Lohi ◽

Jorma Paavonen ◽

...

Keyword(s):

Cause Of Death ◽

Medical Records ◽

Retrospective Cohort ◽

Causes Of Death ◽

Postmortem Examination ◽

University Hospital ◽

Obstetrics And Gynecology ◽

Common Cause ◽

Examination Protocol

AbstractBackgroundStillbirth often remains unexplained, mostly due to a lack of any postmortem examination or one that is incomplete and misinterpreted.MethodsThis retrospective cohort study was conducted at the Department of Obstetrics and Gynecology, Helsinki University Hospital, Finland, and comprised 214 antepartum singleton stillbirths from 2003 to 2015. Maternal and fetal characteristics and the results of the systematic postmortem examination protocol were collected from medical records. Causes of death were divided into 10 specific categories. Re-evaluation of the postmortem examination results followed.ResultsBased on our systematic protocol, the cause of death was originally defined and reported as such to parents in 133 (62.1%) cases. Re-evaluation of the postmortem examination results revealed the cause of death in an additional 43 (20.1%) cases, with only 23 (10.7%) cases remaining truly unexplained. The most common cause of stillbirth was placental insufficiency in 56 (26.2%) cases. A higher proportion of stillbirths that occurred at ≥39 gestational weeks remained unexplained compared to those that occurred earlier (24.1% vs. 8.6%) (P = 0.02).ConclusionA standardized postmortem examination and a re-evaluation of the results reduced the rate of unexplained stillbirth. Better knowledge of causes of death may have a major impact on the follow-up and outcome of subsequent pregnancies. Also, closer examination and better interpretation of postmortem findings is time-consuming but well worth the effort in order to provide better counseling for the grieving parents.

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Causes of death among Syrian children using verbal autopsy

Eastern Mediterranean Health Journal ◽

10.26719/1996.2.3.440 ◽

2021 ◽

Vol 2 (3) ◽

pp. 440-448

Author(s):

N. Abou Rashid ◽

S. Al Jirf ◽

H. Bashour

Keyword(s):

Public Health ◽

Cause Of Death ◽

Verbal Autopsy ◽

Congenital Malformations ◽

Causes Of Death ◽

Public Health Importance ◽

Related Factors ◽

Children Under Five ◽

The Public ◽

Under Five

The causes of death in children under five years were studied using a structured verbal autopsy questionnaire. Possible determinants of death were also investigated. About 44% of deaths were among neonates [below 28 days of age] ; the major causes of death in neonates were prematurity [33%] and birth-related factors [30%]. In infants [1-11 months of age], the leading cause of death was congenital malformations [24%]. Accidents were responsible for one-third of deaths in children aged 1-4 years. Factors that might have contributed to death were investigated. The public health importance of causes of death was evaluated and its implications were discussed

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Mortality Cause in Goldsmith Workers of Bangladesh: Findings From Verbal Autopsy

10.21203/rs.3.rs-508983/v1 ◽

2021 ◽

Author(s):

Fahmida Afroz Khan ◽

Md. Khalequzzaman ◽

Mohammad Tanvir Islam ◽

Ataur Rahman ◽

Shahrin Emdad Rayna ◽

...

Keyword(s):

Life Expectancy ◽

Cause Of Death ◽

Verbal Autopsy ◽

Causes Of Death ◽

Heart Diseases ◽

Control Program ◽

Ischemic Heart ◽

World Health ◽

Average Life Expectancy ◽

Health Organization

Abstract Background: Information on the mortality causes of goldsmith workers in Bangladesh is very limited. This study was conducted to find out the causes of death in this group of population.Methods: The study subject was deceased goldsmith workers where face-to-face interviews were conducted with the family members who were present during the deceased's illness preceding death. A World Health Organization recommended questionnaire was adapted to conduct 20 deceased goldsmith workers' verbal autopsy. Causes of death were determined by reviewing the outcomes of the interviews by the expert physicians.Results: The mean age of the goldsmith workers at death was 59.2 ± 9.3 years. Among the deceased goldsmith workers, 70.0% were smokers, and 50.0% of them were alcohol consumers. Cardiovascular diseases (CVD) were the most common immediate and underlying cause of death (55.0% and 45.0%, respectively). Acute ischemic heart disease was the single most common (30.0%) immediate cause of death among the deceased goldsmith workers, whereas, for underlying causes of death, it was both acute and chronic ischemic heart diseases (35.0%).Conclusions: The life expectancy of goldsmith workers was much lower than the average life expectancy of Bangladesh, where CVD was the common cause of death. Smoking and alcohol consumption were prevalent among the majority of the deceased goldsmith workers. Awareness of healthy lifestyles should be prioritized for a successful CVD control program for this population. Trial registration: Not applicable.

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Comparing causes of death of Hodgkin lymphoma and breast cancer patients between medical records and cause-of-death statistics

Clinical Epidemiology ◽

10.2147/clep.s161359 ◽

2018 ◽

Vol Volume 10 ◽

pp. 1523-1531 ◽

Cited By ~ 1

Author(s):

Simone de Vries ◽

Michael Schaapveld ◽

Jan Kardaun ◽

Kim de Bruin ◽

Augustinus Krol ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Hodgkin Lymphoma ◽

Cause Of Death ◽

Medical Records ◽

Causes Of Death ◽

Breast Cancer Patients

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ADJUDICATION PILOT STUDY OF CAUSE OF DEATH IN THE LONG LIFE FAMILY STUDY

Innovation in Aging ◽

10.1093/geroni/igz038.3372 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S926-S926

Author(s):

Habibatou Diallo ◽

Joanne Murabito ◽

Anne B Newman ◽

Thomas T Perls ◽

Diane Ives ◽

...

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Pilot Study ◽

Cause Of Death ◽

Medical Records ◽

Causes Of Death ◽

Death Certificate ◽

Family Study ◽

Underlying Causes ◽

Long Life

Abstract Background: Death certificate inaccuracy increases at older ages. The Long Life Family Study (LLFS) utilizes a physician adjudication committee to review the death certificate, medical records and a family narrative about cause of death. We report here the adjudication process and the prevalent underlying causes of death for a subsample of those who have died so far. Methods: We first describe the adjudication process. There were ~1,250 deaths in LLFS. We report underlying causes of death for a subset of proband generation subjects enrolled and evaluated by two LLFS study centers. Results: As of May 2019, we have adjudicated 190 deaths (98 male, 92 female) . Mean age 95 years (range 81-105 years). Top 5 causes of death for men: cancer (13%), coronary heart disease (CHD, 13%), dementia (13%), "other" (11%) and "unknown" (9%) and for women: dementia (21%), valvular heart disease (14%), coronary heart disease (12%), unknown (12%) and other (9%). Rate of death due to dementia was greater in women compared to men (CHI2 =7.33, p=0.006). Conclusions: In this pilot study, a significantly greater proportion of women died due to dementia compared to men. At least some portion of this difference may be due to the observation that women are known to survive chronic aging-related diseases more than men and thus have a greater opportunity to die from dementia at advanced ages. An additional cause to consider includes clinicians’ gender bias in ascribing diagnoses in the medical records that were relied upon as part of the adjudication process.

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Singh’s verbal autopsy questionnaire for the assessment of causes of death, social autopsy, tobacco autopsy and dietary autopsy, based on medical records and interview

Acta Cardiologica ◽

10.1080/ac.66.4.2126596 ◽

2011 ◽

Vol 66 (4) ◽

pp. 471-481 ◽

Cited By ~ 5

Author(s):

Ram B. Singh ◽

Jan Fedacko ◽

Viola Vargova ◽

Adarsh Kumar ◽

Varun Mohan ◽

...

Keyword(s):

Verbal Autopsy ◽

Medical Records ◽

Causes Of Death ◽

Social Autopsy

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Overview of Statistical and Machine Learning Techniques for Determining Causes of Death from Verbal Autopsies: A Systematic Literature Review

10.21203/rs.3.rs-95087/v1 ◽

2020 ◽

Author(s):

Michael Tonderai Mapundu ◽

Chodziwadziwa Kabudula ◽

Eustasius Musenge ◽

Turgay Celik

Keyword(s):

Machine Learning ◽

Statistical Methods ◽

Cause Of Death ◽

Verbal Autopsy ◽

Causes Of Death ◽

Machine Learning Techniques ◽

Health Priorities ◽

Learning Approaches ◽

Learning Techniques ◽

Physician Diagnosis

Abstract Background: The process of determining causes of death in areas where there is limited clinical services using verbal autopsies has become a key issue in terms of accuracy on cause of death (prone to errors and subjective), quality of data among many drawbacks. This is mainly because there is no proper standard available in performing verbal autopsy, even though it is important for civil registration systems and strengthening of health priorities. Physician diagnosis is the only gold standard in reviewing verbal autopsy narratives. In practice, conventional statistical methods are used to perform verbal autopsies due to their simplicity and transparency. However, in literature complex machine learning models can be found that can replace the traditional statistical methods. There has not been much application of machine learning techniques in verbal autopsy to determine cause of death, despite the advances in technology. As such, there is a need for a thorough survey of recent literature on statistical and machine learning approaches applied in verbal autopsy to determine cause of death. Methods: A systematic review was conducted and included a search from six databases. Our study only included scientiﬁc articles published in last decade that reported on verbal autopsy and: (1) algorithms; (2) statistical techniques; (3) machine learning and (4) deep learning. The search yielded 110 articles, after meta analysis, we identiﬁed 85 articles as being relevant and discarded the other 25. We investigated and compared the most commonly used statistical and machine learning techniques in VAs, identiﬁed limitations of each of these techniques, proposed a guiding machine learning framework and pointed to future directions. Results: Eighty ﬁve studies met the inclusion criteria. Apart from physician diagnosis, statistical methods are the most currently applied tools to determine cause of death from verbal autopsies. However, there has been little application of traditional machine learning and emerging techniques, even though they have shown promising results in other domains. Conclusions: Technological application of machine learning to determine cause of death, should focus on effective ideal strategies of pre-processing, transparency, robust feature engineering techniques and data balancing in order to attain optimal model performance.

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Improving methods to measure comparable mortality cause (IMMCMC) gold standard verbal autopsy dataset

BMC Research Notes ◽

10.1186/s13104-021-05834-y ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Riley H. Hazard ◽

Hafizur Rahman Chowdhury ◽

Abraham D. Flaxman ◽

Jonathan C. Joseph ◽

Nurul Alam ◽

...

Keyword(s):

Cause Of Death ◽

Verbal Autopsy ◽

Gold Standard ◽

Causes Of Death ◽

Research Study ◽

The Philippines ◽

Study Data ◽

Data Description ◽

Underlying Causes ◽

Death Data

Abstract Objectives Gold standard cause of death data is critically important to improve verbal autopsy (VA) methods in diagnosing cause of death where civil and vital registration systems are inadequate or poor. As part of a three-country research study—Improving Methods to Measure Comparable Mortality by Cause (IMMCMC) study—data were collected on clinicopathological criteria-based gold standard cause of death from hospital record reviews with matched VAs. The purpose of this data note is to make accessible a de-identified format of these gold standard VAs for interested researchers to improve the diagnostic accuracy of VA methods. Data description The study was conducted between 2011 and 2014 in the Philippines, Bangladesh, and Papua New Guinea. Gold standard diagnoses of underlying causes of death for deaths occurring in hospital were matched to VAs conducted using a standardized VA questionnaire developed by the Population Health Metrics Consortium. 3512 deaths were collected in total, comprised of 2491 adults (12 years and older), 320 children (28 days to 12 years), and 702 neonates (0–27 days).

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Five-year mortality rate in an ophthalmic ward in Zimbabwe

African Vision and Eye Health ◽

10.4102/aveh.v74i1.292 ◽

2015 ◽

Vol 74 (1) ◽

Author(s):

Pamidzai Madzima ◽

Rangarirai Masanganise

Keyword(s):

Mortality Rate ◽

Cause Of Death ◽

Medical Records ◽

Ocular Trauma ◽

Hospital Death ◽

Ocular Surface Squamous Neoplasia ◽

Review Period ◽

Referral Hospitals ◽

Death Notification ◽

Non Hodgkins Lymphoma

Objective: To determine the mortality rate in the ophthalmic ward at the Sekuru KaguviHospital Eye Unit (SKHEU), Zimbabwe, from January 2009 to December 2013 and to identify the causes of death.Design: A hospital-based clinical audit.Methods: Data from completed hospital death notification forms and medical records of patients who had died in the ward during the review period were collected that included age, gender, diagnoses, date of admission, date of death and cause of death. The data were analysed using Epi Info7 software.Setting: The audit was conducted at SKHEU, the largest tertiary eye unit in Zimbabwe. It comprises an ophthalmic ward with 30 beds and caters for about 900 patients per year. Patients are admitted via the Out-patients Department where about 30 000 patients are seen yearly. SKHEU is part of the Parirenyatwa Group of Hospitals, which is one of the four major referral hospitals in Zimbabwe.Results: Of the 4722 ophthalmic admissions at SKHEU during the 5-year study period, therewere 15 (0.3%) deaths, with a male:female ratio of 3:2 giving a mortality rate of 0.3% and an average of 3 deaths per year. The highest number of deaths was in 2009 when 7 deaths occurred, whilst the lowest number was 1 death in 2010 and 1 in 2013. Of the 15 deaths,4 (26.7%) were children < 12 years old and 11 (73.3%) were adults; of whom 12 (80%) patients had orbital malignancies, 2 (13.3%) had orbital cellulitis and 1 (6.7%) had ocular trauma. Theorbital malignancies included ocular surface squamous neoplasia (OSSN), retinoblastomaand non-Hodgkins lymphoma (NHL). The most common probable cause of death was OSSN which accounted for 9 (60%) of the deaths.Conclusion: The mortality rate at SKHEU was 0.3%, with approximately 3 deaths occurring per year. The most common attributable cause of death was OSSN.

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