arXiv subjects

Jon Wakefield

Publications and source records attributed to Jon Wakefield.

At least 55 records · Page 3Linked to original sources

arXiv2020

Estimating efficacy of measles supplementary immunization activities via discrete-time modeling of disease incidence time series

Measles is a significant source of global disease burden and child mortality. Measles vaccination through routine immunization (RI) programs in high-burden settings remains a challenge due to poor health care infrastructure and access. Supplementary immunization activities (SIA) in the form of vaccination campaigns are therefore implemented to prevent measles outbreaks by reducing the size of the susceptible population. The SIA efficacy, defined as the fraction of susceptible population immunized by an SIA, is a critical metric for assessing campaign effectiveness. We propose a discrete-time hidden Markov model for estimating SIA efficacy and forecasting future incidence trends using reported measles incidence data. Our approach extends the time-series susceptible-infected-recovered (TSIR) framework by adding a model component to capture the impact of SIAs on the susceptible population. It also accounts for under-reporting and its associated uncertainty via a two-stage estimation procedure with uncertainty propagation. The proposed model can be used to estimate the underlying susceptible population dynamics, assess how many susceptible people were immunized by past SIAs, and forecast incidence trends in the future under various hypothetical SIA scenarios. We examine model performance via simulations under various levels of under-reporting, and apply the model to analyze monthly reported measles incidence in Benin from 2012 to 2018.

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arXiv2020

Estimating the Stillbirth Rate for 195 Countries Using A Bayesian Sparse Regression Model with Temporal Smoothing

Estimation of stillbirth rates globally is complicated because of the paucity of reliable data from countries where most stillbirths occur. We compiled data and developed a Bayesian hierarchical temporal sparse regression model for estimating stillbirth rates for all countries from 2000 to 2019. The model combines covariates with a temporal smoothing process so that estimates are data-driven in country-periods with high-quality data and deter-mined by covariates for country-periods with limited or no data. Horseshoepriors are used to encourage sparseness. The model adjusts observations with alternative stillbirth definitions and accounts for bias in observations that are subject to non-sampling errors. In-sample goodness of fit and out-of-sample validation results suggest that the model is reasonably well calibrated. The model is used by the UN Inter-agency Group for Child Mortality Estimation to monitor the stillbirth rate for all countries.

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arXiv2020

Estimation of Health and Demographic Indicators with Incomplete Geographic Information

In low and middle income countries, household surveys are a valuable source of information for a range of health and demographic indicators. Increasingly, subnational estimates are required for targeting interventions and evaluating progress towards targets. In the majority of cases, stratified cluster sampling is used, with clusters corresponding to enumeration areas. The reported geographical information varies. A common procedure, to preserve confidentiality, is to give a jittered location with the true centroid of the cluster is displaced under a known algorithm. An alternative situation, which was used for older surveys in particular, is to report the geographical region within the cluster lies. In this paper, we describe a spatial hierarchical model in which we account for inaccuracies in the cluster locations. The computational algorithm we develop is fast and avoids the heavy computation of a pure MCMC approach. We illustrate by simulation the benefits of the model, over naive alternatives.

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arXiv2020

Space-time smoothing models for sub-national measles routine immunization coverage estimation with complex survey data

Despite substantial advances in global measles vaccination, measles disease burden remains high in many low- and middle-income countries. A key public health strategy for controling measles in such high-burden settings is to conduct supplementary immunization activities (SIAs) in the form of mass vaccination campaigns, in addition to delivering scheduled vaccination through routine immunization (RI) programs. To achieve balanced implementations of RI and SIAs, robust measurement of sub-national RI-specific coverage is crucial. In this paper, we develop a space-time smoothing model for estimating RI-specific coverage of the first dose of measles-containing-vaccines (MCV1) at sub-national level using complex survey data. The application that motivated this work is estimation of the RI-specific MCV1 coverage in Nigeria's 36 states and the Federal Capital Territory. Data come from four Demographic and Health Surveys, three Multiple Indicator Cluster Surveys, and two National Nutrition and Health Surveys conducted in Nigeria between 2003 and 2018. Our method incorporates information from the SIA calendar published by the World Health Organization and accounts for the impact of SIAs on the overall MCV1 coverage, as measured by cross-sectional surveys. The model can be used to analyze data from multiple surveys with different data collection schemes and construct coverage estimates with uncertainty that reflects the various sampling designs. Implementation of our method can be done efficiently using integrated nested Laplace approximation (INLA).

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arXiv2020

Small Area Estimation of Health Outcomes

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area-level and at the unit-level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015-2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to COVID-19 is discussed.

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arXiv2020

Bayesian Multiresolution Modeling Of Georeferenced Data

Current implementations of multiresolution methods are limited in terms of possible types of responses and approaches to inference. We provide a multiresolution approach for spatial analysis of non-Gaussian responses using latent Gaussian models and Bayesian inference via integrated nested Laplace approximation (INLA). The approach builds on `LatticeKrig', but uses a reparameterization of the model parameters that is intuitive and interpretable so that modeling and prior selection can be guided by expert knowledge about the different spatial scales at which dependence acts. The priors can be used to make inference robust and integration over model parameters allows for more accurate posterior estimates of uncertainty. The extended LatticeKrig (ELK) model is compared to a standard implementation of LatticeKrig (LK), and a standard Matérn model, and we find modest improvement in spatial oversmoothing and prediction for the ELK model for counts of secondary education completion for women in Kenya collected in the 2014 Kenya demographic health survey. Through a simulation study with Gaussian responses and a realistic mix of short and long scale dependencies, we demonstrate that the differences between the three approaches for prediction increases with distance to nearest observation.

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arXiv2020

Modeling and presentation of vaccination coverage estimates using data from household surveys

It is becoming increasingly popular to produce high-resolution maps of vaccination coverage by fitting Bayesian geostatistical models to data from household surveys. Often, the surveys adopt a stratified cluster sampling design. We discuss a number of crucial choices with respect to two key aspects of the map production process: the acknowledgment of the survey design in modeling, and the appropriate presentation of estimates and their uncertainties. Specifically, we consider the importance of accounting for survey stratification and cluster-level non-spatial excess variation in survey outcomes when fitting geostatistical models. We also discuss the trade-off between the geographical scale and precision of model-based estimates, and demonstrate visualization methods for mapping and ranking that emphasize the probabilistic interpretation of results. A novel approach to coverage map presentation is proposed to allow comparison and control of the overall map uncertainty level. We use measles vaccination coverage in Nigeria as a motivating example and illustrate the different issues using data from the 2018 Nigeria Demographic and Health Survey.

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arXiv2019

Design- and Model-Based Approaches to Small-Area Estimation in a Low and Middle Income Country Context: Comparisons and Recommendations

The need for rigorous and timely health and demographic summaries has provided the impetus for an explosion in geographic studies, with a common approach being the production of pixel-level maps, particularly in low and middle income countries. In this context, household surveys are a major source of data, usually with a two-stage cluster design with stratification by region and urbanicity. Accurate estimates are of crucial interest for precision public health policy interventions, but many current studies take a cavalier approach to acknowledging the sampling design, while presenting results at a fine geographic scale. In this paper we investigate the extent to which accounting for sample design can affect predictions at the aggregate level, which is usually the target of inference. We describe a simulation study in which realistic sampling frames are created for Kenya, based on population and demographic information, with a survey design that mimics a Demographic Health Survey (DHS). We compare the predictive performance of various commonly-used models. We also describe a cluster level model with a discrete spatial smoothing prior that has not been previously used, but provides reliable inference. We find that including stratification and cluster level random effects can improve predictive performance. Spatially smoothed direct (weighted) estimates were robust to priors and survey design. Continuous spatial models performed well in the presence of fine scale variation; however, these models require the most "hand holding". Subsequently, we examine how the models perform on real data; specifically we model the prevalence of secondary education for women aged 20-29 using data from the 2014 Kenya DHS.

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arXiv2018

Child Mortality Estimation Incorporating Summary Birth History Data

The United Nations' Sustainable Development Goal 3.2 aims to reduce under-5 child mortality to 25 deaths per 1,000 live births by 2030. Child mortality tends to be concentrated in developing regions where much of the information needed to assess achievement of this goal comes from surveys and censuses. In both, women are asked about their birth histories, but with varying degrees of detail. Full birth history (FBH) data contain the reported dates of births and deaths of every surveyed mother's children. In contrast, summary birth history (SBH) data contain only the total number of children born and total number of children who died for each mother. Specialized methods are needed to accommodate this type of data into analyses of child mortality trends. We develop a data augmentation scheme within a Bayesian framework where for SBH data, birth and death dates are introduced as auxiliary variables. Since we specify a full probability model for the data, many of the well-known biases that exist in this data can be accommodated, along with space-time smoothing on the underlying mortality rates. We illustrate our approach in a simulation, showing that uncertainty is reduced when incorporating SBH data over simply analyzing all available FBH data. We also apply our approach to data in the Central region of Malawi. We compare with the well-known Brass method.

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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

Spatio-Temporal Analysis of Surveillance Data

In this chapter, we consider space-time analysis of surveillance count data. Such data are ubiquitous and a number of approaches have been proposed for their analysis. We first describe the aims of a surveillance endeavor, before reviewing and critiquing a number of common models. We focus on models in which time is discretized to the time scale of the latent and infectious periods of the disease under study. In particular, we focus on the time series SIR (TSIR) models originally described by Finkenstadt and Grenfell in their 2000 paper and the epidemic/endemic models first proposed by Held, Hohle, and Hofmann in their 2005 paper. We implement both of these models in the Stan software and illustrate their performance via analyses of measles data collected over a 2-year period in 17 regions in the Weser-Ems region of Lower Saxony, Germany.

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arXiv2017

Pointless Continuous Spatial Surface Reconstruction

The analysis of area-level aggregated summary data is common in many disciplines including epidemiology and the social sciences. Typically, Markov random field spatial models have been employed to acknowledge spatial dependence and allow data-driven smoothing. In this paper, we exploit recent theoretical and computational advances in continuous spatial modeling to carry out the reconstruction of an underlying continuous spatial surface. In particular, we focus on models based on stochastic partial differential equations (SPDEs). We also consider the interesting case in which the aggregate data are supplemented with point data. We carry out Bayesian inference, and in the language of generalized linear mixed models, if the link is linear, an efficient implementation of the model is available via integrated nested Laplace approximations. For nonlinear links, we present two approaches: a fully Bayesian implementation using a Hamiltonian Monte Carlo algorithm, and an empirical Bayes implementation, that is much faster, and is based on Laplace approximations. We examine the properties of the approach using simulation, and then estimate an underlying continuous risk surface for the classic Scottish lip cancer data.

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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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arXiv2017

Efficient data augmentation for fitting stochastic epidemic models to prevalence data

Stochastic epidemic models describe the dynamics of an epidemic as a disease spreads through a population. Typically, only a fraction of cases are observed at a set of discrete times. The absence of complete information about the time evolution of an epidemic gives rise to a complicated latent variable problem in which the state space size of the epidemic grows large as the population size increases. This makes analytically integrating over the missing data infeasible for populations of even moderate size. We present a data augmentation Markov chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic epidemic model parameters, in which measurements are augmented with subject-level disease histories. In our MCMC algorithm, we propose each new subject-level path, conditional on the data, using a time-inhomogeneous continuous-time Markov process with rates determined by the infection histories of other individuals. The method is general, and may be applied, with minimal modifications, to a broad class of stochastic epidemic models. We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school.

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arXiv2016

Spatial Modeling, with Application to Complex Survey Data: Discussion of "Model-based Geostatistics for Prevalence Mapping in Low-Resource Settings", by Diggle and Giorgi

Prevalence mapping in low resource settings is an increasingly important endeavor to guide policy making and to spatially and temporally characterize the burden of disease. We will focus our discussion on consideration of the complex design when analyzing survey data, and on spatial modeling. With respect to the former, we consider two approaches: direct use of the weights, and a model-based approach using a spatial model to acknowledge clustering. For the latter we consider continuously indexed Markovian Gaussian random field models.

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arXiv2016

Space-time smoothing of complex survey data: Small area estimation for child mortality

Many people living in low- and middle-income countries are not covered by civil registration and vital statistics systems. Consequently, a wide variety of other types of data, including many household sample surveys, are used to estimate health and population indicators. In this paper we combine data from sample surveys and demographic surveillance systems to produce small area estimates of child mortality through time. Small area estimates are necessary to understand geographical heterogeneity in health indicators when full-coverage vital statistics are not available. For this endeavor spatio-temporal smoothing is beneficial to alleviate problems of data sparsity. The use of conventional hierarchical models requires careful thought since the survey weights may need to be considered to alleviate bias due to nonrandom sampling and nonresponse. The application that motivated this work is an estimation of child mortality rates in five-year time intervals in regions of Tanzania. Data come from Demographic and Health Surveys conducted over the period 1991-2010 and two demographic surveillance system sites. We derive a variance estimator of under five years child mortality that accounts for the complex survey weighting. For our application, the hierarchical models we consider include random effects for area, time and survey and we compare models using a variety of measures including the conditional predictive ordinate (CPO). The method we propose is implemented via the fast and accurate integrated nested Laplace approximation (INLA).

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arXiv2015

Restricted Covariance Priors with Applications in Spatial Statistics

We present a Bayesian model for area-level count data that uses Gaussian random effects with a novel type of G-Wishart prior on the inverse variance--covariance matrix. Specifically, we introduce a new distribution called the truncated G-Wishart distribution that has support over precision matrices that lead to positive associations between the random effects of neighboring regions while preserving conditional independence of non-neighboring regions. We describe Markov chain Monte Carlo sampling algorithms for the truncated G-Wishart prior in a disease mapping context and compare our results to Bayesian hierarchical models based on intrinsic autoregression priors. A simulation study illustrates that using the truncated G-Wishart prior improves over the intrinsic autoregressive priors when there are discontinuities in the disease risk surface. The new model is applied to an analysis of cancer incidence data in Washington State.

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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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