Multivariate Bayesian spatial model of preterm birth and cardiovascular disease among Georgia women: Evidence for life course social determinants of health

A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed.

Biopsychosocial Factors during the Perinatal Period: Risks, Preventative Factors, and Implications for Healthcare Professionals

Women face risks to their wellbeing during the perinatal period of pregnancy. However, there is a dearth of information on perinatal risk factors within the biopsychosocial paradigm. Emphasis is often placed on biological components associated with pregnancy and women's health. However, psychological and social determinants of health are integral during the perinatal period, and mental wellness is often a determinant for positive maternal and neonatal health outcomes. This article reviews risk factors of perinatal wellness (e.g., physical and nutritional concerns, trauma, discrimination, adverse childhood events) and highlights protective factors for women in their perinatal period. Healthcare professionals can support perinatal health by focusing on culturally and contextually appropriate research and prevention, providing equal access to sexual and reproductive healthcare information and services, providing quality education and training for helping professionals, and supporting policies for positive sexual and reproductive women's healthcare.

The bivariate combined model for spatial data analysis

To describe the spatial distribution of diseases, a number of methods have been proposed to model relative risks within areas. Most models use Bayesian hierarchical methods, in which one models both spatially structured and unstructured extra-Poisson variance present in the data. For modelling a single disease, the conditional autoregressive (CAR) convolution model has been very popular. More recently, a combined model was proposed that 'combines' ideas from the CAR convolution model and the well-known Poisson-gamma model. The combined model was shown to be a good alternative to the CAR convolution model when there was a large amount of uncorrelated extra-variance in the data. Less solutions exist for modelling two diseases simultaneously or modelling a disease in two sub-populations simultaneously. Furthermore, existing models are typically based on the CAR convolution model. In this paper, a bivariate version of the combined model is proposed in which the unstructured heterogeneity term is split up into terms that are shared and terms that are specific to the disease or subpopulation, while spatial dependency is introduced via a univariate or multivariate Markov random field. The proposed method is illustrated by analysis of disease data in Georgia (USA) and Limburg (Belgium) and in a simulation study. We conclude that the bivariate combined model constitutes an interesting model when two diseases are possibly correlated. As the choice of the preferred model differs between data sets, we suggest to use the new and existing modelling approaches together and to choose the best model via goodness-of-fit statistics. Copyright © 2016 John Wiley & Sons, Ltd.

The role of social determinants in explaining racial/ethnic disparities in perinatal outcomes

In the United States, there continue to be significant racial/ethnic disparities in preterm birth (PTB) rates, infant mortality, and fetal mortality rates. One potential mediator of these disparities is social determinants of health, including individual socioeconomic factors; community factors such as crime, poverty, housing, and the racial/ethnic makeup of the community; and the physical environment. Previous work has identified statistically significant associations between each of these factors and adverse pregnancy outcomes. However, there are recent studies that provide new, innovative insights into this subject, including adding social determinant data to population-based datasets; exploring multiple constructs in their analysis; and examining environmental factors. The objective of this review will be to examine this recent research on the association of each of these sets of social determinants on racial/ethnic disparities PTB, infant mortality, and fetal mortality to highlight potential areas for targeted intervention to reduce these differences.