The neighborhood deprivation gradient and child physical abuse and neglect: A Bayesian spatial model

BACKGROUND

Child abuse and neglect is a public health priority due to its long-term maladaptive consequences. No study in the USA has assessed the nature and magnitude of the social deprivation effect on substantiated child maltreatment risk.

OBJECTIVES

To examine linear and non-linear relationships between area level deprivation and the log-risk of both substantiated physical abuse and neglect while accounting for spatial and heterogeneous random effects.

METHODS

Substantiated child maltreatment and population data (2008-2015) were aggregated to neighborhoods in Bernalillo County, New Mexico. The contribution of area level deprivation to the geographical variation in the log-risks of substantiated child physical abuse and neglect was modeled using Bayesian spatial regression.

RESULTS

Forty-three percent and 46.4 % of the 153 neighborhoods recorded greater risk for either substantiated physical abuse or neglect compared to the county average. The most deprived 20 % of neighborhoods had 71 % and 72 % more cases of substantiated physical abuse and neglect, respectively, than would be expected if the substantiations were randomly distributed throughout the county. Area level deprivation explained 47 % of the variation in substantiated physical abuse and 51 % of the variation in substantiated neglect after controlling for both spatial autocorrelation and heterogeneity.

CONCLUSIONS

Implications from this study can be used to quantify disparities in substantiated child maltreatment attributed to regional differences in social deprivation and to identify priority areas for intervention.

Using multi-sourced big data to correlate sleep deprivation and road traffic noise: A US county-level ecological study

BACKGROUND

Road traffic noise is a serious public health problem globally as it has adverse psychological and physiologic effects (i.e., sleep). Since previous studies mainly focused on individual levels, we aim to examine associations between road traffic noise and sleep deprivation on a large scale; namely, the US at county level.

METHODS

Information from a large-scale sleep survey and national traffic noise map, both obtained from government's open data, were utilized and processed with Geographic Information System (GIS) techniques. To examine the associations between traffic noise and sleep deprivation, we used a hierarchical Bayesian spatial modelling framework to simultaneously adjust for multiple socioeconomic factors while accounting for spatial correlation.

FINDINGS

With 62.90% of people not getting enough sleep, a 10 dBA increase in average sound-pressure level (SPL) or Ls10 (SPL of the relatively noisy area) in a county, was associated with a 49% (OR: 1.49; 95% CrIs:1.19-1.86) or 8% (1.08; 1.00-1.16) increase in the odds of a person in a particular county not getting enough sleep. No significant association was observed for Ls90 (SPL of the relatively quiet area). A 10% increase in noise exposure area or population ratio was associated with a 3% (1.03; 1.01-1.06) or 4% (1.04; 1.02-1.06) increase in the odds of a person within a county not getting enough sleep.

INTERPRETATION

Traffic noise can contribute to variations in sleep deprivation among counties. This study suggests that policymakers could set up different noise-management strategies for relatively quiet and noisy areas and incorporate geospatial noise indicators, such as exposure population or area ratio. Furthermore, urban planners should consider urban sprawl patterns differently in terms of noise-induced sleep problems.

Modeling the spatial patterns of antenatal care utilization in Nigeria with inference based on Pólya-Gamma mixtures

Despite the vast advantages of making antenatal care visits, the service utilization among pregnant women in Nigeria is suboptimal. A five-year monitoring estimate indicated that about 24% of the women who had live births made no visit. The non-utilization induced excessive zeroes in the outcome of interest. Thus, this study adopted a zero-inflated negative binomial model within a Bayesian framework to identify the spatial pattern and the key factors hindering antenatal care utilization in Nigeria. We overcome the intractability associated with posterior inference by adopting a Pólya-Gamma data-augmentation technique to facilitate inference. The Gibbs sampling algorithm was used to draw samples from the joint posterior distribution. Results revealed that type of place of residence, maternal level of education, access to mass media, household work index, and woman's working status have significant effects on the use of antenatal care services. Findings identified substantial state-level spatial disparity in antenatal care utilization across the country. Cost-effective techniques to achieve an acceptable frequency of utilization include the creation of a community-specific awareness to emphasize the importance and benefits of the appropriate utilization. Special consideration should be given to older pregnant women, women in poor antenatal utilization states, and women residing in poor road network regions.

Spatial risk distribution and determinants of E. coli contamination in household drinking water: a case study of Bangladesh

The Escherichia coli (E. coli) contamination in the household (HH) drinking water is often a public health concern. Very few studies explore the associated factors and spatial risk modeling together for E. coli contamination in Bangladesh, this research gap motivates to explore this fact further by utilizing Bangladesh Multiple Indicator Cluster Survey (MICS) 2012-13 data. A Bayesian spatial ordered logit model was used to examine the associated factors and spatial risks of the E. coli contamination. The results show that 62% of HH water samples were contaminated with E. coli. After controlling for different factors, a high level of E. coli contamination was observed among HHs who had access to non-improved water sources. Moreover, no significant rural-urban difference was observed. The spatial prediction of the high-risk contamination was prominent in districts like Dhaka and Bandarban. The study findings can provide insights into the planning of policy activities in Bangladesh.

Sociodemographic and geographical inequalities in under- and overnutrition among children and mothers in Bangladesh: a spatial modelling approach to a nationally representative survey

OBJECTIVE

To investigate the sociodemographic and geographical variation in under- and overnutrition prevalence among children and mothers.

DESIGN

Data from the 2014 Bangladesh Demographic and Health Survey were analysed. Stunting and wasting for children and BMI<18·5 kg/m2 for mothers were considered as undernutrition; overweight was considered as overnutrition for both children and mothers. We estimated the prevalence and performed simple logistic regression analyses to assess the associations between outcome variables and predictors. Bayesian spatial models were applied to estimate region-level prevalence to identify the regions (districts) prone to under- and overnutrition.Settings/SubjectsChildren aged<5 years and their mothers aged 15-49 years in Bangladesh.

RESULTS

A significant difference (P<0·001) was observed in both under- and overnutrition prevalence between poor and rich. A notable regional variation was also observed in under- and overnutrition prevalence. Stunting prevalence ranged from 20·3 % in Jessore to 56·2 % in Sunamgonj, wasting from 10·6 % in Dhaka to 19·2 % in Bhola, and overweight from 0·8 % in Shariatpur to 2·6 % in Dhaka. Of the sixty-four districts, twelve had prevalence of stunting and thirty-two districts had prevalence of wasting higher than the WHO critical threshold levels. Similarly, fifty-three districts had prevalence of maternal underweight higher than the national level. In contrast, the prevalence of overweight was comparatively high in the industrially equipped metropolitan districts.

CONCLUSIONS

Observed sociodemographic and geographical inequalities imply slow progress in the overall improvement of both under- and overnutrition. Therefore, effective intervention programmes and policies need to be designed urgently targeting the grass-roots level of such regions.

Exploring links between greenspace and sudden unexpected death: A spatial analysis

Greenspace has been increasingly recognized as having numerous health benefits. However, its effects are unknown concerning sudden unexpected death (SUD), commonly referred to as sudden cardiac death, which constitutes a large proportion of mortality in the United States. Because greenspace can promote physical activity, reduce stress and buffer air pollutants, it may have beneficial effects for people at risk of SUD, such as those with heart disease, hypertension, and diabetes mellitus. Using several spatial techniques, this study explored the relationship between SUD and greenspace. We adjudicated 396 SUD cases that occurred from March 2013 to February 2015 among reports from emergency medical services (EMS) that attended out-of-hospital deaths in Wake County (central North Carolina, USA). We measured multiple greenspace metrics in each census tract, including the percentages of forest, grassland, average tree canopy, tree canopy diversity, near-road tree canopy and greenway density. The associations between SUD incidence and these greenspace metrics were examined using Poisson regression (non-spatial) and Bayesian spatial models. The results from both models indicated that SUD incidence was inversely associated with both greenway density (adjusted risk ratio [RR] = 0.82, 95% credible/ confidence interval [CI]: 0.69-0.97) and the percentage of forest (adjusted RR = 0.90, 95% CI: 0.81-0.99). These results suggest that increases in greenway density by 1 km/km2 and in forest by 10% were associated with a decrease in SUD risk of 18% and 10%, respectively. The inverse relationship was not observed between SUD incidence and other metrics, including grassland, average tree canopy, near-road tree canopy and tree canopy diversity. This study implies that greenspace, specifically greenways and forest, may have beneficial effects for people at risk of SUD. Further studies are needed to investigate potential causal relationships between greenspace and SUD, and potential mechanisms such as promoting physical activity and reducing stress.

A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities

Relating disease status to imaging data stands to increase the clinical significance of neuroimaging studies. Many neurological and psychiatric disorders involve complex, systems-level alterations that manifest in functional and structural properties of the brain and possibly other clinical and biologic measures. We propose a Bayesian hierarchical model to predict disease status, which is able to incorporate information from both functional and structural brain imaging scans. We consider a two-stage whole brain parcellation, partitioning the brain into 282 subregions, and our model accounts for correlations between voxels from different brain regions defined by the parcellations. Our approach models the imaging data and uses posterior predictive probabilities to perform prediction. The estimates of our model parameters are based on samples drawn from the joint posterior distribution using Markov Chain Monte Carlo (MCMC) methods. We evaluate our method by examining the prediction accuracy rates based on leave-one-out cross validation, and we employ an importance sampling strategy to reduce the computation time. We conduct both whole-brain and voxel-level prediction and identify the brain regions that are highly associated with the disease based on the voxel-level prediction results. We apply our model to multimodal brain imaging data from a study of Parkinson's disease. We achieve extremely high accuracy, in general, and our model identifies key regions contributing to accurate prediction including caudate, putamen, and fusiform gyrus as well as several sensory system regions.

Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa

Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources.This article is part of the themed issue 'One Health for a changing world: zoonoses, ecosystems and human well-being'.

The effect of road network patterns on pedestrian safety: A zone-based Bayesian spatial modeling approach

Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong. The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data.

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

BACKGROUND

There is epidemiologic evidence that women who experience preterm birth (PTB) are at elevated risk for cardiovascular disease (CVD) later in life. Each outcome independently has noted spatial and socioeconomic gradients; we test for spatial structure in the population correlation of the two.

METHODS

Exploratory spatial data analysis and multivariate Bayesian spatial models were fit to describe the spatial correlation of PTB with CVD among women in Georgia counties from 2002 to 2006.

RESULTS

Global Moran's I and local-indicators of spatial association statistics suggest significant co-occurrence of CVD and PTB. Bayesian posterior estimates for multivariate correlation of these outcomes range from r=0.11-0.34 for CVD and PTB. Significant spatial correlation persists with control for county covariates among whites but not blacks.

CONCLUSION

Modest evidence for spatial structure of the ecologic correlation of PTB and women's CVD is consistent with a lifecourse perspective on socially clustered determinants of health.