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

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).

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

2021 ◽  
Author(s):  
Lei Chen ◽  
Tian Xia ◽  
Rasika Rampatige ◽  
Hang Li ◽  
Tim Adair ◽  
...  
Keyword(s):  
Cause Of Death ◽  
Verbal Autopsy ◽  
Medical Records ◽  
Community Health Centers ◽  
Causes Of Death ◽  
Practical Method ◽  
Initial Diagnosis ◽  
Hospital Death ◽  
Routine Practice ◽  
Record Review

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.

Mortality Profile Related to Systemic Lupus Erythematosus: A Multiple Cause-of-death Analysis

2012 ◽  
Vol 39 (3) ◽  
pp. 496-503 ◽  
Author(s):  
DEBORAH C.C. SOUZA ◽  
AUGUSTO H. SANTO ◽  
EMILIA I. SATO
Keyword(s):  
Renal Failure ◽  
Cause Of Death ◽  
Lupus Erythematosus ◽  
Digestive System ◽  
Causes Of Death ◽  
Circulatory System ◽  
Systemic Lupus ◽  
Circulatory System Diseases ◽  
Underlying Causes ◽  
Digestive System Diseases

Objective.To analyze the mortality profile related to systemic lupus erythematosus (SLE) in the state of São Paulo, Brazil.Methods.For the 1985–2007 period, we analyzed all death certificates (n = 4815) on which SLE was listed as an underlying (n = 3133) or non-underlying (n = 1682) cause of death. We evaluated sex, age, and the causes of death, comparing the first and last 5 years of the period, as well as determining the observed/expected death ratio (O/E ratio).Results.For SLE as an underlying cause, the mean age at death was 35.77 years (SD 15.12) and the main non-underlying causes of death were renal failure, circulatory system diseases, pneumonia, and septicemia. Over the period, the proportional mention of infectious causes and circulatory system diseases increased, whereas renal diseases decreased. For SLE as a non-underlying cause of death, the most common underlying causes of death were circulatory, respiratory, genitourinary, and digestive system diseases, and certain infections. The overall death O/E ratio was > 1 for renal failure, tuberculosis, septicemia, pneumonia, and digestive system diseases, as well as for circulatory system diseases at < 50 years of age, particularly acute myocardial infarct.Conclusion.Unlike in developed countries, renal failure and infectious diseases are still the most frequent causes of death. The increase in SLE deaths associated with infection, especially pneumonia and septicemia, is worrisome. The judicious use of immunosuppressive therapy together with vigorous treatment of cardiovascular comorbidities is crucial to the successful management of SLE and to improving survival of patients with SLE.

scholarly journals Causes of death among Syrian children using verbal autopsy

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

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

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.

Abstract P047: US Asian Indians Dying With Atherosclerosis Are More Likely To Have Concurrent Diabetes Mellitus

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Deepak R Nair ◽  
Abhyuday Chauhan ◽  
Dhananjay Vaidya
Keyword(s):  
Diabetes Mellitus ◽  
Cause Of Death ◽  
Asian Indians ◽  
Causes Of Death ◽  
High Prevalence ◽  
The Us ◽  
Icd 10 ◽  
The Difference ◽  
Public Health Strategies ◽  
Death Data

Background: Asian Indians (AI) in the US are known to have a high prevalence of atherosclerotic diseases and diabetes mellitus (DM). However, it is not known if these two cluster as causes of death in AI compared to the rest of US population. Methods: Using 2017 Multiple Cause-of-Death Data from the National Centre for Health Statistics, we included deaths at age ≥ 45 years among US residents where AI vs not-AI could be ascertained (n = 7940 AI; n = 2.6 million not-AI). DM (ICD-10 range: E10-E14) and any atherosclerosis (either of: ischemic heart disease, ischemic stroke, atherosclerosis; ICD-10 range: I20-I25, I69, I70, respectively) as contributing causes of death were identified in AI and not-AI. We calculated dichotomous tetrachoric correlation (Rho) between DM and atherosclerosis as co-occurring contributing causes. To examine whether this association differed by age decade and sex, we calculated the difference in fraction of deaths with DM in those with atherosclerosis versus those without atherosclerosis as a co-occurring cause of death. Results: There were 114,210 atherosclerosis deaths and 24,331 DM deaths in 2017 in the sample. DM and atherosclerosis as contributing causes correlated more strongly in AI (Rho = 0.36, p < 0.001) as compared to not-AI (Rho = 0.31, p < 0.001; difference between groups, p < 0.001). The excess fraction of deaths due to DM when atherosclerosis also contributed vs when atherosclerosis did not contribute was higher in AI men of all ages and in most ages for AI women, except for a group where AI death numbers were smaller (Figure). Conclusion: Our findings highlight the increased burden of co-occurring DM and atherosclerotic disease together contributing as causes of death in AI compared to not-AI in the US. Public health strategies targeted to AI should focus on prevention and clinical treatment of both conditions jointly, in both men and women, especially during young adulthood and middle age.

Competing Causes of Death: An Analysis using Multiple-Cause-of-Death Data from The Netherlands

1995 ◽  
Vol 141 (5) ◽  
pp. 466-475 ◽  
Author(s):  
J. P. Mackenbach ◽  
A. E. Kunst ◽  
H. Lautenbach ◽  
F. Bijlsma ◽  
Y. B. Oei
Keyword(s):  
The Netherlands ◽  
Cause Of Death ◽  
Causes Of Death ◽  
Competing Causes ◽  
Death Data

scholarly journals A Deep Artificial Neural Network−Based Model for Prediction of Underlying Cause of Death From Death Certificates: Algorithm Development and Validation

10.2196/17125 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e17125 ◽  
Author(s):  
Louis Falissard ◽  
Claire Morgand ◽  
Sylvie Roussel ◽  
Claire Imbaud ◽  
Walid Ghosn ◽  
...  
Keyword(s):  
Neural Network ◽  
Cause Of Death ◽  
Deep Neural Network ◽  
Causes Of Death ◽  
State Of The Art ◽  
Death Certificates ◽  
Current State ◽  
Underlying Causes ◽  
Artificial Neural ◽  
Underlying Cause Of Death

Background Coding of underlying causes of death from death certificates is a process that is nowadays undertaken mostly by humans with potential assistance from expert systems, such as the Iris software. It is, consequently, an expensive process that can, in addition, suffer from geospatial discrepancies, thus severely impairing the comparability of death statistics at the international level. The recent advances in artificial intelligence, specifically the rise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problems that were typically considered out of reach without human assistance; they require a considerable amount of data to learn from, which is typically their main limiting factor. However, the CépiDc (Centre d’épidémiologie sur les causes médicales de Décès) stores an exhaustive database of death certificates at the French national scale, amounting to several millions of training examples available for the machine learning practitioner. Objective This article investigates the application of deep neural network methods to coding underlying causes of death. Methods The investigated dataset was based on data contained from every French death certificate from 2000 to 2015, containing information such as the subject’s age and gender, as well as the chain of events leading to his or her death, for a total of around 8 million observations. The task of automatically coding the subject’s underlying cause of death was then formulated as a predictive modelling problem. A deep neural network−based model was then designed and fit to the dataset. Its error rate was then assessed on an exterior test dataset and compared to the current state-of-the-art (ie, the Iris software). Statistical significance of the proposed approach’s superiority was assessed via bootstrap. Results The proposed approach resulted in a test accuracy of 97.8% (95% CI 97.7-97.9), which constitutes a significant improvement over the current state-of-the-art and its accuracy of 74.5% (95% CI 74.0-75.0) assessed on the same test example. Such an improvement opens up a whole field of new applications, from nosologist-level batch-automated coding to international and temporal harmonization of cause of death statistics. A typical example of such an application is demonstrated by recoding French overdose-related deaths from 2000 to 2010. Conclusions This article shows that deep artificial neural networks are perfectly suited to the analysis of electronic health records and can learn a complex set of medical rules directly from voluminous datasets, without any explicit prior knowledge. Although not entirely free from mistakes, the derived algorithm constitutes a powerful decision-making tool that is able to handle structured medical data with an unprecedented performance. We strongly believe that the methods developed in this article are highly reusable in a variety of settings related to epidemiology, biostatistics, and the medical sciences in general.

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

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.

scholarly journals 110. The Burden and Preventability of Sepsis-Associated Mortality in 6 US Acute Care Hospitals

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S1-S2
Author(s):  
Chanu Rhee ◽  
Travis Jones ◽  
Yasir Hamad ◽  
Anupam Pande ◽  
Jack Varon ◽  
...  
Keyword(s):  
Medical Record ◽  
Acute Care ◽  
Cause Of Death ◽  
Causes Of Death ◽  
Clinical Care ◽  
Acute Care Hospitals ◽  
Source Control ◽  
Solid Cancer ◽  
Underlying Causes ◽  
The Impact

Abstract Background Sepsis is considered a leading cause of preventable death, but the actual burden of sepsis mortality is difficult to measure using administrative data or death certificates. We analyzed the prevalence, underlying causes, and preventability of deaths due to sepsis in acute care hospitals using detailed medical record reviews. Methods We randomly selected 577 adult patients who died in-hospital or were discharged to hospice in 2014–2015 at 6 US academic and community hospitals for medical record review. Cases were reviewed by experienced clinicians for sepsis during hospitalization (using Sepsis-3 criteria), terminal conditions on admission (defined using hospice-qualifying criteria), immediate and underlying causes of death, and suboptimal sepsis care (delays in antibiotics, inappropriate antibiotic therapy, inadequate source control, or other medical errors). The overall preventability of death was rated on a 6-point Likert scale (from definitely not preventable to definitely preventable) taking into account comorbidities, severity of illness, and quality of care. Results Sepsis was present in 302/577 (52%) hospitalizations ending in death or discharge to hospice and was the immediate cause of death in 199 cases (35%) (Figure 1A). Underlying causes of death in sepsis patients included solid cancer (21%) and chronic heart disease (15%), and hematologic cancer (10%) (Figure 1B). The median age of sepsis patients who died was 73 (IQR 62–84). Terminal conditions were present in 122/302 (40%) sepsis deaths, most commonly end-stage cancer (26% of cases). Suboptimal care was identified in 68 (23%) of sepsis deaths, most commonly delays in antibiotics (11% of cases). However, only 4% of sepsis deaths were definitely or likely preventable and an additional 8% were considered possibly preventable with optimal clinical care (Figures 2 and 3). Conclusion Our findings affirm that sepsis is the most common cause of death in hospitalized patients. Most patients that died with sepsis were elderly with severe comorbidities, but up to 1 in 8 sepsis deaths were felt to be potentially preventable with better hospital-based care. These findings may inform resource allocation and expectations surrounding the impact of hospital-based sepsis treatment initiatives. Disclosures All authors: No reported disclosures.

scholarly journals Machine-learning model to predict the cause of death using a stacking ensemble method for observational data

2020 ◽  
Author(s):  
Chungsoo Kim ◽  
Seng Chan You ◽  
Jenna M Reps ◽  
Jae Youn Cheong ◽  
Rae Woong Park
Keyword(s):  
Machine Learning ◽  
Cause Of Death ◽  
Causes Of Death ◽  
External Validation ◽  
Learning Model ◽  
Ensemble Method ◽  
Common Data Model ◽  
Neoplastic Disease ◽  
Machine Learning Model ◽  
Death Data

Abstract Objective Cause of death is used as an important outcome of clinical research; however, access to cause-of-death data is limited. This study aimed to develop and validate a machine-learning model that predicts the cause of death from the patient’s last medical checkup. Materials and Methods To classify the mortality status and each individual cause of death, we used a stacking ensemble method. The prediction outcomes were all-cause mortality, 8 leading causes of death in South Korea, and other causes. The clinical data of study populations were extracted from the national claims (n = 174 747) and electronic health records (n = 729 065) and were used for model development and external validation. Moreover, we imputed the cause of death from the data of 3 US claims databases (n = 994 518, 995 372, and 407 604, respectively). All databases were formatted to the Observational Medical Outcomes Partnership Common Data Model. Results The generalized area under the receiver operating characteristic curve (AUROC) of the model predicting the cause of death within 60 days was 0.9511. Moreover, the AUROC of the external validation was 0.8887. Among the causes of death imputed in the Medicare Supplemental database, 11.32% of deaths were due to malignant neoplastic disease. Discussion This study showed the potential of machine-learning models as a new alternative to address the lack of access to cause-of-death data. All processes were disclosed to maintain transparency, and the model was easily applicable to other institutions. Conclusion A machine-learning model with competent performance was developed to predict cause of death.

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