arXiv subjects

Samuel J. Clark

Publications and source records attributed to Samuel J. Clark.

31 records · Page 2Linked to original sources

arXiv2018

Bayesian factor models for probabilistic cause of death assessment with verbal autopsies

The distribution of deaths by cause provides crucial information for public health planning, response, and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease epidemiology. Verbal autopsy (VA) surveys are increasingly used in such settings to collect information on the signs, symptoms, and medical history of people who have recently died. This article develops a novel Bayesian method for estimation of population distributions of deaths by cause using verbal autopsy data. The proposed approach is based on a multivariate probit model where associations among items in questionnaires are flexibly induced by latent factors. Using the Population Health Metrics Research Consortium labeled data that include both VA and medically certified causes of death, we assess performance of the proposed method. Further, we estimate important questionnaire items that are highly associated with causes of death. This framework provides insights that will simplify future data collection.

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arXiv2018

Quantifying the Contributions of Training Data and Algorithm Logic to the Performance of Automated Cause-assignment Algorithms for Verbal Autopsy

A verbal autopsy (VA) consists of a survey with a relative or close contact of a person who has recently died. VA surveys are commonly used to infer likely causes of death for individuals when deaths happen outside of hospitals or healthcare facilities. Several statistical and algorithmic methods are available to assign cause of death using VA surveys. Each of these methods require as inputs some information about the joint distribution of symptoms and causes. In this note, we examine the generalizability of this symptom-cause information by comparing different automated coding methods using various combinations of inputs and evaluation data. VA algorithm performance is affected by both the specific SCI themselves and the logic of a given algorithm. Using a variety of performance metrics for all existing VA algorithms, we demonstrate that in general the adequacy of the information about the joint distribution between symptoms and cause affects performance at least as much or more than algorithm logic.

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arXiv2018

A Guide to Comparing the Performance of VA Algorithms

The literature comparing the performance of algorithms for assigning cause of death using verbal autopsy data is fractious and does not reach a consensus on which algorithms perform best, or even how to do the comparison. This manuscript explains the challenges and suggests a way forward. A universal challenge is the lack of standard training and testing data. This limits meaningful comparisons between algorithms, and further, limits the ability of any algorithm to classify verbal autopsy deaths by cause in a way that is widely generalizable across regions and through time. Verbal autopsy algorithms utilize a variety of information to describe the relationship between verbal autopsy symptoms and causes of death - called symptom-cause information (SCI). A crowd sourced, public archive of SCI managed by the World Health Organization (WHO) is suggested as a way to address the lack of SCI for developing, testing, and comparing verbal autopsy coding algorithms, and additionally, as a way to ensure that algorithm-assigned causes of death are as accurate and comparable across regions and through time as possible.

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arXiv2017

Estimating Under Five Mortality in Space and Time in a Developing World Context

Accurate estimates of the under-5 mortality rate (U5MR) in a developing world context are a key barometer of the health of a nation. This paper describes new models to analyze survey data on mortality in this context. We are interested in both spatial and temporal description, that is, wishing to estimate U5MR across regions and years, and to investigate the association between the U5MR and spatially-varying covariate surfaces. We illustrate the methodology by producing yearly estimates for subnational areas in Kenya over the period 1980 - 2014 using data from demographic health surveys (DHS). We use a binomial likelihood with fixed effects for the urban/rural stratification to account for the complex survey design. We carry out smoothing using Bayesian hierarchical models with continuous spatial and temporally discrete components. A key component of the model is an offset to adjust for bias due to the effects of HIV epidemics. Substantively, there has been a sharp decline in U5MR in the period 1980 - 2014, but large variability in estimated subnational rates remains. A priority for future research is understanding this variability. Temperature, precipitation and a measure of malaria infection prevalence were candidates for inclusion in the covariate model.

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arXiv2017

Hyak Mortality Monitoring System: Innovative Sampling and Estimation Methods - Proof of Concept by Simulation

Traditionally health statistics are derived from civil and/or vital registration. Civil registration in low-income countries varies from partial coverage to essentially nothing at all. Consequently the state of the art for public health information in low-income countries is efforts to combine or triangulate data from different sources to produce a more complete picture across both time and space - data amalgamation. Data sources amenable to this approach include sample surveys, sample registration systems, health and demographic surveillance systems, administrative records, census records, health facility records and others. We propose a new statistical framework for gathering health and population data - Hyak - that leverages the benefits of sampling and longitudinal, prospective surveillance to create a cheap, accurate, sustainable monitoring platform. Hyak has three fundamental components: 1) Data Amalgamation: a sampling and surveillance component that organizes two or more data collection systems to work together: a) data from HDSS with frequent, intense, linked, prospective follow-up and b) data from sample surveys conducted in large areas surrounding the Health and Demographic Surveillance System sites using informed sampling so as to capture as many events as possible; 2) Cause of Death: verbal autopsy to characterize the distribution of deaths by cause at the population level; and 3) SES: measurement of socioeconomic status in order to characterize poverty and wealth. We conduct a simulation study of the informed sampling component of Hyak based on the Agincourt HDSS site in South Africa. Compared to traditional cluster sampling, Hyak's informed sampling captures more deaths, and when combined with an estimation model that includes spatial smoothing, produces estimates mortality that have lower variance and small bias.

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arXiv2016

A General Age-Specific Mortality Model with An Example Indexed by Child or Child/Adult Mortality

BACKGROUND. The majority of countries in Africa and nearly one third of all countries require mortality models to infer complete age schedules of mortality, required for population estimates, projections/forecasts and many other tasks in demography and epidemiology. Models that relate child mortality to mortality at other ages are important because all countries have measures of child mortality. OBJECTIVE. 1) Design a general model for age-specific mortality that provides a standard way to relate covariates to age-specific mortality. 2) Calibrate that model using the relationship between child or child/adult mortality and mortality at other ages. 3) Validate the calibrated model and compare its performance to existing models. METHODS. A general, parametrizable component model of mortality is designed using the singular value decomposition (SVD-Comp) and calibrated to the relationship between child or child/adult mortality and mortality at other ages in the observed mortality schedules of the Human Mortality Database. Cross validation is used to validate the model, and the predictive performance of the model is compared to that of the Log-Quad model, designed to do the same thing. RESULTS. Prediction and cross validation tests indicate that the child mortality-calibrated SVD-Comp is able to accurately represent the observed mortality schedules in the Human Mortality Database, is robust to the selection of mortality schedules used to calibrate it, and performs better than the Log-Quad Model. CONCLUSIONS. The child mortality-calibrated SVD-Comp is a useful tool that can be used where child mortality is available but mortality at other ages is unknown. Together with earlier work on an HIV prevalence-calibrated version of SVD-Comp, this work suggests that this approach is truly general and could be used to develop a wide range of additional useful models.

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arXiv2016

Probabilistic Population Projections for Countries with Generalized HIV/AIDS Epidemics

The United Nations (UN) issued official probabilistic population projections for all countries to 2100 in July 2015. This was done by simulating future levels of total fertility and life expectancy from Bayesian hierarchical models, and combining the results using a standard cohort-component projection method. The 40 countries with generalized HIV/AIDS epidemics were treated differently from others, in that the projections used the highly multistate Spectrum/EPP model, a complex 15-compartment model that was designed for short-term projections of quantities relevant to policy for the epidemic. Here we propose a simpler approach that is more compatible with the existing UN probabilistic projection methodology for other countries. Changes in life expectancy are projected probabilistically using a simple time series regression model on current life expectancy, HIV prevalence and ART coverage. These are then converted to age- and sex-specific mortality rates using a new family of model life tables designed for countries with HIV/AIDS epidemics that reproduces the characteristic hump in middle adult mortality. These are then input to the standard cohort-component method, as for other countries. The method performed well in an out-of-sample cross-validation experiment. It gives similar population projections to Spectrum/EPP in the short run, while being simpler and avoiding multistate modeling.

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arXiv2015

Probabilistic Cause-of-death Assignment using Verbal Autopsies

In regions without complete-coverage civil registration and vital statistics systems there is uncertainty about even the most basic demographic indicators. In such areas the majority of deaths occur outside hospitals and are not recorded. Worldwide, fewer than one-third of deaths are assigned a cause, with the least information available from the most impoverished nations. In populations like this, verbal autopsy (VA) is a commonly used tool to assess cause of death and estimate cause-specific mortality rates and the distribution of deaths by cause. VA uses an interview with caregivers of the decedent to elicit data describing the signs and symptoms leading up to the death. This paper develops a new statistical tool known as InSilicoVA to classify cause of death using information acquired through VA. InSilicoVA shares uncertainty between cause of death assignments for specific individuals and the distribution of deaths by cause across the population. Using side-by-side comparisons with both observed and simulated data, we demonstrate that InSilicoVA has distinct advantages compared to currently available methods.

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arXiv2015

A Singular Value Decomposition-based Factorization and Parsimonious Component Model of Demographic Quantities Correlated by Age: Predicting Complete Demographic Age Schedules with Few Parameters

BACKGROUND. Formal demography has a long history of building simple models of age schedules of demographic quantities, e.g. mortality and fertility rates. These are widely used in demographic methods to manipulate whole age schedules using few parameters. OBJECTIVE. The Singular Value Decomposition (SVD) factorizes a matrix into three matrices with useful properties including the ability to reconstruct the original matrix using many fewer, simple matrices. This work demonstrates how these properties can be exploited to build parsimonious models of whole age schedules of demographic quantities that can be further parameterized in terms of arbitrary covariates. METHODS. The SVD is presented and explained in detail with attention to developing an intuitive understanding. The SVD is used to construct a general, component model of demographic age schedules, and that model is demonstrated with age-specific mortality and fertility rates. Finally, the model is used (1) to predict age-specific mortality using HIV indicators and summary measures of age-specific mortality, and (2) to predict age-specific fertility using the total fertility rate (TFR). RESULTS. The component model of age-specific mortality and fertility rates succeeds in reproducing the data with two inputs, and acting through those two inputs, various covariates are able to accurately predict full age schedules. CONCLUSIONS. The SVD is potentially useful as a way to summarize, smooth and model age-specific demographic quantities. The component model is a general method of relating covariates to whole age schedules. COMMENTS. The focus of this work is the SVD and the component model. The applications are for illustrative purposes only.

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arXiv2015

InSilicoVA: A Method to Automate Cause of Death Assignment for Verbal Autopsy

Verbal autopsies (VA) are widely used to provide cause-specific mortality estimates in developing world settings where vital registration does not function well. VAs assign cause(s) to a death by using information describing the events leading up to the death, provided by care givers. Typically physicians read VA interviews and assign causes using their expert knowledge. Physician coding is often slow, and individual physicians bring bias to the coding process that results in non-comparable cause assignments. These problems significantly limit the utility of physician-coded VAs. A solution to both is to use an algorithmic approach that formalizes the cause-assignment process. This ensures that assigned causes are comparable and requires many fewer person-hours so that cause assignment can be conducted quickly without disrupting the normal work of physicians. Peter Byass' InterVA method is the most widely used algorithmic approach to VA coding and is aligned with the WHO 2012 standard VA questionnaire. The statistical model underpinning InterVA can be improved; uncertainty needs to be quantified, and the link between the population-level CSMFs and the individual-level cause assignments needs to be statistically rigorous. Addressing these theoretical concerns provides an opportunity to create new software using modern languages that can run on multiple platforms and will be widely shared. Building on the overall framework pioneered by InterVA, our work creates a statistical model for automated VA cause assignment.

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arXiv2014

Clustering South African households based on their asset status using latent variable models

The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. A model-based approach to clustering the Agincourt households, based on latent variable models, is proposed. In the case of modeling binary or ordinal items, item response theory models are employed. For nominal survey items, a factor analysis model, similar in nature to a multinomial probit model, is used. Both model types have an underlying latent variable structure - this similarity is exploited and the models are combined to produce a hybrid model capable of handling mixed data types. Further, a mixture of the hybrid models is considered to provide clustering capabilities within the context of mixed binary, ordinal and nominal response data. The proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD). The MFA-MD model is applied to the survey data to cluster the Agincourt households into homogeneous groups. The model is estimated within the Bayesian paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings result, providing insight to the different socio-economic strata within the Agincourt region.

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arXiv2013

Bayesian Reconstruction of Two-Sex Populations by Age: Estimating Sex Ratios at Birth and Sex Ratios of Mortality

The original version of Bayesian reconstruction, a method for estimating age-specific fertility, mortality, migration and population counts of the recent past with uncertainty, produced estimates for female-only populations. Here we show how two-sex populations can be similarly reconstructed and probabilistic estimates of various sex ratio quantities obtained. We demonstrate the method by reconstructing the populations of India from 1971 to 2001, Thailand from 1960 to 2000, and Laos from 1985 to 2005. We found evidence that in India, sex ratio at birth exceeded its conventional upper limit of 1.06, and, further, increased over the period of study, with posterior probability above 0.9. In addition, almost uniquely, we found evidence that life expectancy at birth was lower for females than for males in India (posterior probability for 1971--1976 0.79), although there was strong evidence for a narrowing of the gap through to 2001. In both Thailand and Laos, we found strong evidence for the more usual result that life expectancy at birth was greater for females and, in Thailand, that the difference increased over the period of study.

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arXiv2007

Probabilistic projections of HIV prevalence using Bayesian melding

The Joint United Nations Programme on HIV/AIDS (UNAIDS) has developed the Estimation and Projection Package (EPP) for making national estimates and short-term projections of HIV prevalence based on observed prevalence trends at antenatal clinics. Assessing the uncertainty about its estimates and projections is important for informed policy decision making, and we propose the use of Bayesian melding for this purpose. Prevalence data and other information about the EPP model's input parameters are used to derive a probabilistic HIV prevalence projection, namely a probability distribution over a set of future prevalence trajectories. We relate antenatal clinic prevalence to population prevalence and account for variability between clinics using a random effects model. Predictive intervals for clinic prevalence are derived for checking the model. We discuss predictions given by the EPP model and the results of the Bayesian melding procedure for Uganda, where prevalence peaked at around 28% in 1990; the 95% prediction interval for 2010 ranges from 2% to 7%.

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