Alcohol Availability and Violence: A Closer Look at Space and Time

Alcohol availability plays an important role in violence. Less is known about how spatiotemporal patterns of alcohol–violence association vary across time of day and across various crime types. This study examined whether and how the associations between on- and off-premise alcohol outlets and assaults, and between on- and off-premise alcohol outlets and robberies, vary across different times of day (morning, daytime, evening, and late night). This cross-sectional study used socioeconomic, alcohol license, and crime data from Milwaukee, Wisconsin, aggregated to US Census block groups and estimated spatially lagged maximum likelihood regression models that controlled for spatial dependence. On-premise outlets were negatively associated with evening assaults and positively associated with daytime and late-night robberies. Off-premise outlets were positively associated with evening assaults, late-night assaults, daytime robberies, and evening robberies. Spatiotemporal alcohol–violence associations vary across crime types and across time of day. On- and off-premise alcohol outlets play a unique role across four different temporal categories and across two violent crime types. These findings have the potential to inform theoretical explanations of the alcohol–violence relationship and may be beneficial when considering and designing custom-tailored local alcohol policy to reduce alcohol-related harm

Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review

Background: Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden. Methods: We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO (CRD42016036655). Results: We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff's spatial scan statistic followed by local Moran's I and Getis and Ord's local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined. Conclusions: A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control

Characterizing the spatial determinants and prevention of malaria in Kenya

The United Nations' Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used-Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino-southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.Published versio

Spatial Patterns Associating Low Birth Weight with Environmental and Behavioral Factors

Low birth weight (LBW) is a significant public health problem in the world. It was estimated globally by the World Health Organization (WHO) that prevalence of LBW was 15% of all births. In Murung Raya district LBW cases remain high. This paper aimed to identify and discuss the relationship between environmental risk factors with LBW in Murung Raya.A spatial analysis was conducted with 150 women as the total participantswho were recruited through the incidence data in 2013-2014. The questionnaires, medical records, and geographic data were measured by Stata software, ArcGis, SatScan, and Geoda. The study results indicated there was significant correlation between health behavior and environmental variables with the strength of external neighborhood effect across LBW risk factors. More intense clustering of high values (hot spots) was found through the spatial analysis showing that most of the cases were located near the defined buffer zone. This research demonstrates that the spatial pattern analysis provided greater statistical power to detect an effect that was not apparent in the previous epidemiology studies

The geography of post-disaster mental health: spatial patterning of psychological vulnerability and resilience factors in New York City after Hurricane Sandy

Background: Only very few studies have investigated the geographic distribution of psychological resilience and associated mental health outcomes after natural or man made disasters. Such information is crucial for location-based interventions that aim to promote recovery in the aftermath of disasters. The purpose of this study therefore was to investigate geographic variability of (1) posttraumatic stress (PTS) and depression in a Hurricane Sandy affected population in NYC and (2) psychological vulnerability and resilience factors among affected areas in NYC boroughs. Methods: Cross-sectional telephone survey data were collected 13 to 16 months post-disaster from household residents (N = 418 adults) in NYC communities that were most heavily affected by the hurricane. The Posttraumatic Stress Checklist for DSM-5 (PCL-5) was applied for measuring posttraumatic stress and the nine-item Patient Health Questionnaire (PHQ-9) was used for measuring depression. We applied spatial autocorrelation and spatial regimes regression analyses, to test for spatial clusters of mental health outcomes and to explore whether associations between vulnerability and resilience factors and mental health differed among New York City\u27s five boroughs . Results: Mental health problems clustered predominantly in neighborhoods that are geographically more exposed towards the ocean indicating a spatial variation of risk within and across the boroughs. We further found significant variation in associations between vulnerability and resilience factors and mental health. Race/ethnicity (being Asian or non-Hispanic black) and disaster-related stressors were vulnerability factors for mental health symptoms in Queens, and being employed and married were resilience factors for these symptoms in Manhattan and Staten Island. In addition, parental status was a vulnerability factor in Brooklyn and a resilience factor in the Bronx. Conclusions: We conclude that explanatory characteristics may manifest as psychological vulnerability and resilience factors differently across different regional contexts. Our spatial epidemiological approach is transferable to other regions around the globe and, in the light of a changing climate, could be used to strengthen the psychosocial resources of demographic groups at greatest risk of adverse outcomes pre-disaster. In the aftermath of a disaster, the approach can be used to identify survivors at greatest risk and to plan for targeted interventions to reach them

Geospatial Analysis of Road Traffic Accidents, Injuries and Deaths in Nigeria

RTAs in Nigeria are very high and have become progressively important because of their heavy health and financial burden. The few geographic studies on RTAs in Nigeria are limited to their mere spatial distribution and associated risk factors, with very little attention given to their spatial clustering patterns and the detection of hotspots. With the aid of Global Moran’s I and Local Getis, the study found some evidence of significant positive spatial autocorrelation, and consistent clustering of RTAs, RTIs and RTDs in the southwest from 2002 to 2007 which suggested the presence of an accident belt in the southwestern region, which has been accounted by poor road infrastructure, relatively high level of economic development and high vehicular movements. The study recommends the deployment of road safety officials to the accident belt, strict enforcement of safety belts and helmets, and periodic road maintenance

Does Context Matter for the Relationship between Deprivation and All-Cause Mortality? The West vs. the Rest of Scotland

One of the assumptions that is often made in modeling the relationship between deprivation and mortality is that this relationship will remain the same across space. There is little justification presented in the literature as to why the deprivation-mortality relationship will be homogenous across space. The homogeneity of this relationship over space is an empirical question and most of the published literature does not formally test this relationship. Using postcode data for Scotland (UK), this study addresses this research gap and tests the hypothesis of spatial heterogeneity in the relationship between area-level deprivation and mortality. Research into health inequalities frequently fails to recognise spatial heterogeneity in the deprivation-health relationship, assuming that global relationships apply uniformly across geographical areas. In this study, exploratory spatial data analysis methods are used to assess local patterns in deprivation and mortality. A variety of spatial regression models are then implemented to examine the relationship between deprivation and mortality. The hypothesis of spatial heterogeneity in the relationship between deprivation and mortality is rejected. Implications of the homogeneity of the deprivation-mortality relationships for addressing health inequities are discussed in light of the inverse care law.