Suicide rates vary considerably across U.S. counties. Spatial non-stationarity may explain mixed findings on the relationship between suicide and income inequality.

This ecological study analyzed county-level income inequality and suicide rates for the timespan 2012-2016. Ordinary least squares regression, multilevel regression, and geographically weighted regression models were constructed while adjusting for age, race/ethnicity, gender, education, median income, unemployment, and urbanicity.

Ordinary least squares regression and multilevel models found no significant association between income inequality and county suicide rates after adjusting for confounding variables. However, the geographically weighted regression model identified two main areas in which income inequality was negatively associated with suicide rates, as well as several counties across central U.S. in which income inequality was positively associated with suicide rates.

Income inequality’s effect on county suicide rates may vary across space. Future research should consider spatial non-stationarity when studying suicide and macro-level socioeconomic conditions.

Income inequality’s effect on county suicide rates may vary across space. Future research should consider spatial non-stationarity when studying suicide and macro-level socioeconomic conditions.To understand the spatio-temporal patterns and associated risk factors with the frequency, we analyze records of mental health related emergency department (MHED) visits from youth. The data are extracted for the period 2002–2011 from the population-based, provincial health administrative data systems of Alberta, Canada. Guided by a descriptive analysis, we conduct generalized linear regression analyses of the counts of MHED visits from various health areas. Seasonal effects are examined via three different types of functions, including trigonometric functions. We specify the temporal correlation using an autoregressive model of order 1 and formulate the spatial correlation by a random effects model. Our analysis reveals a strong seasonal pattern and indicates that the MHED visit counts are significantly associated with age, gender, and a proxy for socio-economic status. The final statistical model may be used to forecast future MHED use and identify regions and groups at a higher risk to the MHEDs.In this paper, we compare a variety of spatio-temporal conditional autoregressive models to a dengue fever dataset in Colombia, and incorporate an innovative data transformation method in the data analysis. In order to gain a better understanding on the effects of different niche variables in the epidemiological process, we explore Poisson-lognormal and binomial models with different Bayesian spatio-temporal modeling methods in this paper. Our results show that the selected model can well capture the variations of the data. The population density, elevation, daytime and night land surface temperatures are among the contributory variables to identify potential dengue outbreak regions; precipitation and vegetation variables are not significant in the selected spatio-temporal mixed effects model. The generated dengue fever probability maps from the model show a geographic distribution of risk that apparently coincides with the elevation gradient. The results in the paper provide the most benefits for future work in dengue studies.The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first discovered in late 2019 in Wuhan City, China. The virus may cause novel coronavirus disease 2019 (COVID-19) in symptomatic individuals. Since December of 2019, there have been over 7,000,000 confirmed cases and over 400,000 confirmed deaths worldwide. In the United States (U.S.), there have been over 2,000,000 confirmed cases and over 110,000 confirmed deaths. selleck kinase inhibitor COVID-19 case data in the United States has been updated daily at the county level since the first case was reported in January of 2020. There currently lacks a study that showcases the novelty of daily COVID-19 surveillance using space-time cluster detection techniques. In this paper, we utilize a prospective Poisson space-time scan statistic to detect daily clusters of COVID-19 at the county level in the contiguous 48 U.S. and Washington D.C. As the pandemic progresses, we generally find an increase of smaller clusters of remarkably steady relative risk. Daily tracking of significant space-time clusters can facilitate decision-making and public health resource allocation by evaluating and visualizing the size, relative risk, and locations that are identified as COVID-19 hotspots.Population-level disease risk varies in space and time, and is typically estimated using aggregated disease count data relating to a set of non-overlapping areal units for multiple consecutive time periods. A large research base of statistical models and corresponding software has been developed for such data, with most analyses being undertaken in a Bayesian setting using either Markov chain Monte Carlo (MCMC) simulation or integrated nested Laplace approximations (INLA). This paper presents a tutorial for undertaking spatio-temporal disease modelling using MCMC simulation, utilising the CARBayesST package in the R software environment. The tutorial describes the complete modelling journey, starting with data input, wrangling and visualisation, before focusing on model fitting, model assessment and results presentation. It is illustrated by a new case study of pneumonia mortality at the local authority level in England, and answers important public health questions including the effect of covariate risk factors, spatio-temporal trends, and health inequalities.Avian influenza (AIV) is a highly contagious virus that can infect both wild birds and domestic poultry. This study aimed to define areas within the state of South Carolina (SC) at heightened risk for environmental persistence of AIV using geospatial methods. Environmental factors known to influence AIV survival were identified through the published literature and using a multi-criteria decision analysis with GIS was performed. Risk was defined using five categories following the World Organization for Animal Health Risk Assessment Guidelines. Less than 1% of 1km grid cells in SC showed a high risk of AIV persistence. Approximately 2% – 17% of counties with high or very high environmental risk also had medium to very high numbers of commercial poultry operations. Results can be used to improve surveillance activities and to inform biosecurity practices and emergency preparedness efforts.