Summary Social, economic, environmental and behavioural factors impacting health are well recognized in the literature. We consider the use of various income inequality measures in addition to a poverty measure and investigate their effects on human immunodeficiency virus (HIV) mortality. In doing so, we make use of models that can capture zero inflation and spatiotemporal effects. The research is motivated by the lack of studies from an inference and modelling perspectives in explaining HIV mortality by using measures that take into account socio‐economic status as well as time and location. Such a study can help policy makers to identify cases of environmental injustice and areas of outstanding health risk to assist in resource allocation problems. In our numerical example, we make use of mortality data obtained for the state of New York, estimate model parameters from a Bayesian inference perspective and discuss the implications and interpretations of various income inequality measures. The methodological novelty of our study is the introduction of a zero‐inflated Poisson model that can account for both spatial and temporal effects across 5 years (2000–2004). The practical novelty of our study is its attempt to find inequality measures which can improve our understanding of HIV mortality risk. Our results indicate that, for the data at hand, if inequality is calculated on the basis of county‐specific income shares rather than the whole state, HIV mortality can be better explained. In addition, accounting for temporal and spatial effects was found to contribute to our understanding of HIV mortality risk.

In this paper, we investigate the effects of poverty and inequality on the number of HIV-related deaths in 62 New York counties via Bayesian zero-inflated Poisson models that exhibit spatial dependence. We quantify inequality via the Theil index and poverty via the ratios of two Census 2000 variables, the number of people under the poverty line and the number of people for whom poverty status is determined, in each Zip Code Tabulation Area. The purpose of this study was to investigate the effects of inequality and poverty in addition to spatial dependence between neighboring regions on HIV mortality rate, which can lead to improved health resource allocation decisions. In modeling county-specific HIV counts, we propose Bayesian zero-inflated Poisson models whose rates are functions of both covariate and spatial/random effects. To show how the proposed models work, we used three different publicly available data sets: TIGER Shapefiles, Census 2000, and mortality index files. In addition, we introduce parameter estimation issues of Bayesian zero-inflated Poisson models and discuss MCMC method implications.

The sampling resource allocation decisions for medical audits of outpatient procedures are crucial and challenging because of the large payment amounts and heterogeneity of the claims. A number of frameworks are utilized to help auditors address the trade-offs between efficiency and cost while having valid overpayment amount estimates. As a potential improvement, this paper presents a novel information-theoretic multistage sampling framework. In particular, we propose an iterative stratified sampling method that uses Lindley's entropy measure to evaluate the expected amount of information. We use US Medicare Part B claims outpatient payment data and investigate the versatility of the framework for different overpayment scenarios and resource allocation designs. The proposed method results in reasonable coverage and lower estimation errors for the proportion of overpaid claims and overpayment recovery amounts. Our sampling method is shown to outperform the current stratification method of practice, ie, Neyman allocation, for many scenarios. The framework also can be used to make probability statements on variables of interest, such as the number of overpaid claims.