Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy

Determining causes of deaths (COD) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or "domains") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this paper, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a pre-specified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. Posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation data set. The paper concludes with a discussion on limitations and future directions.

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Integrating community-based verbal autopsy into civil registration and vital statistics (CRVS): system-level considerations

Global Health Action ◽

10.1080/16549716.2017.1272882 ◽

2017 ◽

Vol 10 (1) ◽

pp. 1272882 ◽

Cited By ~ 25

Author(s):

Don de Savigny ◽

Ian Riley ◽

Daniel Chandramohan ◽

Frank Odhiambo ◽

Erin Nichols ◽

...

Keyword(s):

Verbal Autopsy ◽

Vital Statistics ◽

System Level ◽

Community Based ◽

Civil Registration

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Performance of verbal autopsy methods in estimating HIV-associated mortality among adults in South Africa

BMJ Global Health ◽

10.1136/bmjgh-2018-000833 ◽

2018 ◽

Vol 3 (4) ◽

pp. e000833 ◽

Cited By ~ 1

Author(s):

Aaron S Karat ◽

Noriah Maraba ◽

Mpho Tlali ◽

Salome Charalambous ◽

Violet N Chihota ◽

...

Keyword(s):

South Africa ◽

Verbal Autopsy ◽

Vital Statistics ◽

Classification Systems ◽

Hiv Positive ◽

Hiv Status ◽

Civil Registration ◽

Art Initiation ◽

Better Than ◽

Hiv Negative

IntroductionVerbal autopsy (VA) can be integrated into civil registration and vital statistics systems, but its accuracy in determining HIV-associated causes of death (CoD) is uncertain. We assessed the sensitivity and specificity of VA questions in determining HIV status and antiretroviral therapy (ART) initiation and compared HIV-associated mortality fractions assigned by different VA interpretation methods.MethodsUsing the WHO 2012 instrument with added ART questions, VA was conducted for deaths among adults with known HIV status (356 HIV positive and 103 HIV negative) in South Africa. CoD were assigned using physician-certified VA (PCVA) and computer-coded VA (CCVA) methods and compared with documented HIV status.ResultsThe sensitivity of VA questions in detecting HIV status and ART initiation was 84.3% (95% CI 80 to 88) and 91.0% (95% CI 86 to 95); 283/356 (79.5%) HIV-positive individuals were assigned HIV-associated CoD by PCVA, 166 (46.6%) by InterVA-4.03, 201 (56.5%) by InterVA-5, and 80 (22.5%) and 289 (81.2%) by SmartVA-Analyze V.1.1.1 and V.1.2.1. Agreement between PCVA and older CCVA methods was poor (chance-corrected concordance [CCC] <0; cause-specific mortality fraction [CSMF] accuracy ≤56%) but better between PCVA and updated methods (CCC 0.21–0.75; CSMF accuracy 65%–98%). All methods were specific (specificity 87% to 96%) in assigning HIV-associated CoD.ConclusionAll CCVA interpretation methods underestimated the HIV-associated mortality fraction compared with PCVA; InterVA-5 and SmartVA-Analyze V.1.2.1 performed better than earlier versions. Changes to VA methods and classification systems are needed to track progress towards targets for reducing HIV-associated mortality,

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Automated verbal autopsy: from research to routine use in civil registration and vital statistics systems

BMC Medicine ◽

10.1186/s12916-020-01520-1 ◽

2020 ◽

Vol 18 (1) ◽

Cited By ~ 1

Author(s):

Riley H. Hazard ◽

Mahesh P. K. Buddhika ◽

John D. Hart ◽

Hafizur R. Chowdhury ◽

Sonja Firth ◽

...

Keyword(s):

Verbal Autopsy ◽

Communicable Disease ◽

Decision Support Tool ◽

Vital Statistics ◽

Policy Formulation ◽

The Philippines ◽

Study Data ◽

Support Tool ◽

Civil Registration ◽

Disease Mortality

Abstract Background The majority of low- and middle-income countries (LMICs) do not have adequate civil registration and vital statistics (CRVS) systems to properly support health policy formulation. Verbal autopsy (VA), long used in research, can provide useful information on the cause of death (COD) in populations where physicians are not available to complete medical certificates of COD. Here, we report on the application of the SmartVA tool for the collection and analysis of data in several countries as part of routine CRVS activities. Methods Data from VA interviews conducted in 4 of 12 countries supported by the Bloomberg Philanthropies Data for Health (D4H) Initiative, and at different stages of health statistical development, were analysed and assessed for plausibility: Myanmar, Papua New Guinea (PNG), Bangladesh and the Philippines. Analyses by age- and cause-specific mortality fractions were compared to the Global Burden of Disease (GBD) study data by country. VA interviews were analysed using SmartVA-Analyze-automated software that was designed for use in CRVS systems. The method in the Philippines differed from the other sites in that the VA output was used as a decision support tool for health officers. Results Country strategies for VA implementation are described in detail. Comparisons between VA data and country GBD estimates by age and cause revealed generally similar patterns and distributions. The main discrepancy was higher infectious disease mortality and lower non-communicable disease mortality at the PNG VA sites, compared to the GBD country models, which critical appraisal suggests may highlight real differences rather than implausible VA results. Conclusion Automated VA is the only feasible method for generating COD data for many populations. The results of implementation in four countries, reported here under the D4H Initiative, confirm that these methods are acceptable for wide-scale implementation and can produce reliable COD information on community deaths for which little was previously known.

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Civil Registration and Vital Statistics for Monitoring the Sustainable Development Goals

10.1596/27533 ◽

2017 ◽

Cited By ~ 11

Author(s):

Samuel Mills ◽

Carla Abouzahr ◽

Jane Kim ◽

Bahie M. Rassekh ◽

Deborah Sarpong

Keyword(s):

Sustainable Development ◽

Sustainable Development Goals ◽

Vital Statistics ◽

The Sustainable Development ◽

Civil Registration ◽

Development Goals

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Thai Civil Registration and Vital Statistics and Unique Identification Number Systems for Universal Health Coverage

10.1596/32545 ◽

2019 ◽

Cited By ~ 1

Author(s):

Supasit Pannarunothai ◽

Boonchai Kijsanayotin ◽

Samuel Mills

Keyword(s):

Universal Health Coverage ◽

Vital Statistics ◽

Health Coverage ◽

Identification Number ◽

Civil Registration ◽

Number Systems ◽

Universal Health

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Developmental Measurement of Mental Attention

International Journal of Behavioral Development ◽

10.1177/016502549401700110 ◽

1994 ◽

Vol 17 (1) ◽

pp. 161-200 ◽

Cited By ~ 66

Author(s):

Juan Pascual-Leone ◽

Raymond Baillargeon

Keyword(s):

Latent Class ◽

Latent Class Models ◽

Categorical Data Analysis ◽

Test Items ◽

Mental Demand ◽

Mental Attention ◽

Constructivist Model ◽

Discrete Manner ◽

Analysis System ◽

Data Analysis System

A dialectical constructivist model of mental attention ("effort") and of working memory is briefly presented, and used to explicate subjects' processing in misleading test items. We illustrate with task analyses of the Figural Intersections Test (FIT). We semantically derive a set of 10 Theoretical Structural Predictions (TSP) that stipulate relations between mental attentional resources (mental-power: Mp) and the systematically varied mental demand of items (mental-demand: Md), as they jointly codetermine probable performance (conditional probabilities of passing and failing). These predictions are evaluated on first approximation using a known family of ordered Latent Class models, all probabilistic versions of Guttman's unidimensional scale. Parameters of these models were estimated using the Categorical Data Analysis System of Eliason (1990). Main results are: (1) Data fit Lazarsfeld's latent-distance model, providing initial support for our 10 predictions; (2) The M-power of children (latent Mp-classes) when assessed behaviourally may increase with age in a discrete manner, and have the potential to generate interval scales of measurement; (3) In the light of our results what statisticians often consider "error of measurement" appears (in part) to be signal, not noise: The organismic signal of misleading (Y-) processes that in their dialectical (trade-off) interaction with success-producing (X-) processes generate performance.

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Development and temporal external validation of a simple risk score tool for prediction of outcomes after severe head injury based on admission characteristics from level-1 trauma centre of India using retrospectively collected data

BMJ Open ◽

10.1136/bmjopen-2020-040778 ◽

2021 ◽

Vol 11 (1) ◽

pp. e040778

Author(s):

Vineet Kumar Kamal ◽

Ravindra Mohan Pandey ◽

Deepak Agrawal

Keyword(s):

Hospital Mortality ◽

External Validation ◽

Trauma Centre ◽

Unfavourable Outcome ◽

Motor Score ◽

Validation Data ◽

Data Set ◽

Development Data ◽

Level 1 ◽

Pupillary Reactivity

ObjectiveTo develop and validate a simple risk scores chart to estimate the probability of poor outcomes in patients with severe head injury (HI).DesignRetrospective.SettingLevel-1, government-funded trauma centre, India.ParticipantsPatients with severe HI admitted to the neurosurgery intensive care unit during 19 May 2010–31 December 2011 (n=946) for the model development and further, data from same centre with same inclusion criteria from 1 January 2012 to 31 July 2012 (n=284) for the external validation of the model.Outcome(s)In-hospital mortality and unfavourable outcome at 6 months.ResultsA total of 39.5% and 70.7% had in-hospital mortality and unfavourable outcome, respectively, in the development data set. The multivariable logistic regression analysis of routinely collected admission characteristics revealed that for in-hospital mortality, age (51–60, >60 years), motor score (1, 2, 4), pupillary reactivity (none), presence of hypotension, basal cistern effaced, traumatic subarachnoid haemorrhage/intraventricular haematoma and for unfavourable outcome, age (41–50, 51–60, >60 years), motor score (1–4), pupillary reactivity (none, one), unequal limb movement, presence of hypotension were the independent predictors as its 95% confidence interval (CI) of odds ratio (OR)_did not contain one. The discriminative ability (area under the receiver operating characteristic curve (95% CI)) of the score chart for in-hospital mortality and 6 months outcome was excellent in the development data set (0.890 (0.867 to 912) and 0.894 (0.869 to 0.918), respectively), internal validation data set using bootstrap resampling method (0.889 (0.867 to 909) and 0.893 (0.867 to 0.915), respectively) and external validation data set (0.871 (0.825 to 916) and 0.887 (0.842 to 0.932), respectively). Calibration showed good agreement between observed outcome rates and predicted risks in development and external validation data set (p>0.05).ConclusionFor clinical decision making, we can use of these score charts in predicting outcomes in new patients with severe HI in India and similar settings.

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Application of verbal autopsy in routine civil registration in Lusaka District of Zambia

BMC Health Services Research ◽

10.1186/s12913-021-06427-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

C. Chabila Mapoma ◽

Brian Munkombwe ◽

Chomba Mwango ◽

Bupe Bwalya Bwalya ◽

Audrey Kalindi ◽

...

Keyword(s):

Middle Ages ◽

Cause Of Death ◽

Verbal Autopsy ◽

Opportunistic Infections ◽

Civil Registration System ◽

Registration System ◽

Civil Registration ◽

Major Teaching Hospital ◽

External Causes ◽

Causes Of Deaths

Abstract Background Ascertaining the causes for deaths occurring outside health facilities is a significant problem in many developing countries where civil registration systems are not well developed or non-functional. Standardized and rigorous verbal autopsy methods is a potential solution to determine the cause of death. We conducted a demonstration project in Lusaka District of Zambia where verbal autopsy (VA) method was implemented in routine civil registration system. Methods About 3400 VA interviews were conducted for bodies “brought-in-dead” at Lusaka’s two major teaching hospital mortuaries using a SmartVA questionnaire between October 2017 and September 2018. Probable underlying causes of deaths using VA and cause-specific mortality fractions were determined.. Demographic characteristics were analyzed for each VA-ascertained cause of death. Results Opportunistic infections (OIs) associated with HIV/AIDS such as pneumonia and tuberculosis, and malaria were among leading causes of deaths among bodies “brought-in-dead”. Over 21.6 and 26.9% of deaths were attributable to external causes and non-communicable diseases (NCDs), respectively. The VA-ascertained causes of death varied by age-group and sex. External causes were more prevalent among males in middle ages (put an age range like 30–54 years old) and NCDs highly prevalent among those aged 55 years and older. Conclusions VA application in civil registration system can provide the much-needed cause of death information for non-facility deaths in countries with under-developed or non-functional civil registration systems.

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