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

Determining causes of deaths (CODs) 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 article, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a prespecified 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. The 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 dataset. The article concludes with a discussion of limitations and future directions.

Etiology of Acute Lower Respiratory Illness Hospitalizations Among Infants in 4 Countries.

Background: Recent studies explored which pathogens drive the global burden of pneumonia hospitalizations among young children. However, the etiology of broader acute lower respiratory tract infections (ALRIs) remains unclear. Methods: Using a multicountry study (Albania, Jordan, Nicaragua, and the Philippines) of hospitalized infants and non-ill community controls between 2015 and 2017, we assessed the prevalence and severity of viral infections and coinfections. We also estimated the proportion of ALRI hospitalizations caused by 21 respiratory pathogens identified via multiplex real-time reverse transcription polymerase chain reaction with bayesian nested partially latent class models. Results: An overall 3632 hospitalized infants and 1068 non-ill community controls participated in the study and had specimens tested. Among hospitalized infants, 1743 (48.0%) met the ALRI case definition for the etiology analysis. After accounting for the prevalence in non-ill controls, respiratory syncytial virus (RSV) was responsible for the largest proportion of ALRI hospitalizations, although the magnitude varied across sites-ranging from 65.2% (95% credible interval, 46.3%-79.6%) in Albania to 34.9% (95% credible interval, 20.0%-49.0%) in the Philippines. While the fraction of ALRI hospitalizations caused by RSV decreased as age increased, it remained the greatest driver. After RSV, rhinovirus/enterovirus (range, 13.4%-27.1%) and human metapneumovirus (range, 6.3%-12.0%) were the next-highest contributors to ALRI hospitalizations. Conclusions: We observed substantial numbers of ALRI hospitalizations, with RSV as the largest source, particularly in infants aged <3 months. This underscores the potential for vaccines and long-lasting monoclonal antibodies on the horizon to reduce the burden of ALRI in infants worldwide.

Probabilistic cause-of-disease assignment using case-control diagnostic tests: A latent variable regression approach.

Optimal prevention and treatment strategies for a disease of multiple causes, such as pneumonia, must be informed by the population distribution of causes among cases, or cause-specific case fractions (CSCFs). CSCFs may further depend on additional explanatory variables. Existing methodological literature in disease etiology research does not fully address the regression problem, particularly under a case-control design. Based on multivariate binary non-gold-standard diagnostic data and additional covariate information, this article proposes a novel and unified regression modeling framework for estimating covariate-dependent CSCF functions in case-control disease etiology studies. The model leverages critical control data for valid probabilistic cause assignment for cases. We derive an efficient Markov chain Monte Carlo algorithm for flexible posterior inference. We illustrate the inference of CSCF functions using extensive simulations and show that the proposed model produces less biased estimates and more valid inference of the overall CSCFs than analyses that omit covariates. A regression analysis of pediatric pneumonia data reveals the dependence of CSCFs upon season, age, human immunodeficiency virus status and disease severity. The article concludes with a brief discussion on model extensions that may further enhance the utility of the regression model in broader disease etiology research.