No Arabic abstract
Reliable mortality estimates at the subnational level are essential in the study of health inequalities within a country. One of the difficulties in producing such estimates is the presence of small populations, where the stochastic variation in death counts is relatively high, and so the underlying mortality levels are unclear. We present a Bayesian hierarchical model to estimate mortality at the subnational level. The model builds on characteristic age patterns in mortality curves, which are constructed using principal components from a set of reference mortality curves. Information on mortality rates are pooled across geographic space and smoothed over time. Testing of the model shows reasonable estimates and uncertainty levels when the model is applied to both simulated data which mimic US counties, and real data for French departments. The estimates produced by the model have direct applications to the study of subregional health patterns and disparities.
In recent years, much of the focus in monitoring child mortality has been on assessing changes in the under-five mortality rate (U5MR). However, as the U5MR decreases, the share of neonatal deaths (within the first month) tends to increase, warranting increased efforts in monitoring this indicator in addition to the U5MR. A Bayesian splines regression model is presented for estimating neonatal mortality rates (NMR) for all countries. In the model, the relationship between NMR and U5MR is assessed and used to inform estimates, and spline regression models are used to capture country-specific trends. As such, the resulting NMR estimates incorporate trends in overall child mortality while also capturing data-driven trends. The model is fitted to 195 countries using the database from the United Nations Interagency Group for Child Mortality Estimation, producing estimates from 1990, or earlier if data are available, until 2015. The results suggest that, above a U5MR of 34 deaths per 1000 live births, at the global level, a 1 per cent increase in the U5MR leads to a 0.6 per cent decrease in the ratio of NMR to U5MR. Below a U5MR of 34 deaths per 1000 live births, the proportion of deaths under-five that are neonatal is constant at around 54 per cent. However, the relationship between U5MR and NMR varies across countries. The model has now been adopted by the United Nations Inter-agency Group for Child Mortality Estimation.
In order to implement disease-specific interventions in young age groups, policy makers in low- and middle-income countries require timely and accurate estimates of age- and cause-specific child mortality. High quality data is not available in settings where these interventions are most needed, but there is a push to create sample registration systems that collect detailed mortality information. Current methods that estimate mortality from this data employ multistage frameworks without rigorous statistical justification that separately estimate all-cause and cause-specific mortality and are not sufficiently adaptable to capture important features of the data. We propose a flexible Bayesian modeling framework to estimate age- and cause-specific child mortality from sample registration data. We provide a theoretical justification for the framework, explore its properties via simulation, and use it to estimate mortality trends using data from the Maternal and Child Health Surveillance System in China.
Sensor noise sources cause differences in the signal recorded across pixels in a single image and across multiple images. This paper presents a Bayesian approach to decomposing and characterizing the sensor noise sources involved in imaging with digital cameras. A Bayesian probabilistic model based on the (theoretical) model for noise sources in image sensing is fitted to a set of a time-series of images with different reflectance and wavelengths under controlled lighting conditions. The image sensing model is a complex model, with several interacting components dependent on reflectance and wavelength. The properties of the Bayesian approach of defining conditional dependencies among parameters in a fully probabilistic model, propagating all sources of uncertainty in inference, makes the Bayesian modeling framework more attractive and powerful than classical methods for approaching the image sensing model. A feasible correspondence of noise parameters to their expected theoretical behaviors and well calibrated posterior predictive distributions with a small root mean square error for model predictions have been achieved in this study, thus showing that the proposed model accurately approximates the image sensing model. The Bayesian approach could be extended to formulate further components aimed at identifying even more specific parameters of the imaging process.
Accurate estimates of subnational populations are important for policy formulation and monitoring population health indicators. For example, estimates of the number of women of reproductive age are important to understand the population at risk to maternal mortality and unmet need for contraception. However, in many low-income countries, data on population counts and components of population change are limited, and so levels and trends subnationally are unclear. We present a Bayesian constrained cohort component model for the estimation and projection of subnational populations. The model builds on a cohort component projection framework, incorporates census data and estimates from the United Nations World Population Prospects, and uses characteristic mortality schedules to obtain estimates of population counts and the components of population change, including internal migration. The data required as inputs to the model are minimal and available across a wide range of countries, including most low-income countries. The model is applied to estimate and project populations by county in Kenya for 1979-2019, and validated against the 2019 Kenyan census.
The improvement of mortality projection is a pivotal topic in the diverse branches related to insurance, demography, and public policy. Motivated by the thread of Lee-Carter related models, we propose a Bayesian model to estimate and predict mortality rates for multi-population. This new model features in information borrowing among populations and properly reflecting variations of data. It also provides a solution to a long-time overlooked problem: model selection for dependence structures of population-specific time parameters. By introducing a Dirac spike function, simultaneous model selection and estimation for population-specific time effects can be achieved without much extra computation cost. We use the Japanese mortality data from Human Mortality Database to illustrate the desirable properties of our model.