2,838 research outputs found
A Multilevel Model for Comorbid Outcomes: Obesity and Diabetes in the US
Multilevel models are overwhelmingly applied to single health outcomes, but when two or more health conditions are closely related, it is important that contextual variation in their joint prevalence (e.g., variations over different geographic settings) is considered. A multinomial multilevel logit regression approach for analysing joint prevalence is proposed here that includes subject level risk factors (e.g., age, race, education) while also taking account of geographic context. Data from a US population health survey (the 2007 Behavioral Risk Factor Surveillance System or BRFSS) are used to illustrate the method, with a six category multinomial outcome defined by diabetic status and weight category (obese, overweight, normal). The influence of geographic context is partly represented by known geographic variables (e.g., county poverty), and partly by a model for latent area influences. In particular, a shared latent variable (common factor) approach is proposed to measure the impact of unobserved area influences on joint weight and diabetes status, with the latent variable being spatially structured to reflect geographic clustering in risk
A Model for Spatially Varying Crime Rates in English Districts: The Effects of Social Capital, Fragmentation, Deprivation and Urbanicity
Abstract: Geographic variations in crime are often linked to aspects of urban social structure that are latent constructs, not directly observed but instead proxied by a range of observed indicators. Examples are area deprivation and urbanicity, both established risk factors for crime. Little UK based evidence exists for impacts on crime of other potentially relevant influences such as social capital and social fragmentation, which are also latent constructs. Other cited influences on area crime differences include income inequality, but there may be further unobserved factors, which tend to be spatially correlated. The present paper seeks to establish, using appropriate multivariate and spatial regression techniques, the relative importance of social capital, fragmentation, deprivation, urbanicity and income inequality in an analysis of recent crime variations between 324 English Local Authority Districts. Variations in total, violent and property crime are considered
Racial Residential Segregation and COVID-19 Mortality
Studies have shown that the social and physical environments are strong predictors of the health of the urban population. This study investigates if racial residential segregation has any impact on the poor health outcome of residents in light of the COVID-19 pandemic. This is a cross-sectional study with 8, 668, 744 observations at the individual level. The hierarchical logistic regression conducted to investigate the association between race and residential segregation with COVID-19 mortality showed that a one unit increase in segregation is associated with a 1 % increase in mortality. Furthermore, people from Black and Asian ethnic communities were more likely than Whites to die from COVID-19, but Hispanics were less likely to die. This study has limitations such as a modifiable aerial unit problem, as county-level segregation indices were used for the analysis
Reductions in global biodiversity loss predicted from conservation spending
Halting global biodiversity loss is central to both the Convention on Biological Diversity (CBD) and United Nations Sustainable Development Goals (SDGs)1,2, but success to date has been very limited3–5. A critical determinant of overall strategic success (or failure) is the financing committed to biodiversity6–9; however, financing decisions are still hindered by considerable uncertainty over what any investment is likely to achieve6–9.. For greater effectiveness, we need an evidence-based model (EBM)10–12 showing how conservation spending quantitatively reduces the rate of loss. Here, we empirically quantify how i$14.4 billion of conservation investment reduced biodiversity loss across 109 signatory countries between 1996 and 2008, by an average 29% per country. We also show that biodiversity change in signatory countries can be predicted with high accuracy, using a dual model that combines the positive impact of conservation investment with the negative impact of economic, agricultural and population growth (i.e. human development pressures)13–18. Decision-makers can use this dual model to forecast the improvement that any proposed biodiversity budget would achieve under various scenarios of human development pressure, comparing those forecasts to any chosen policy target (including the CBD and SDGs). Importantly, we further find that spending impacts shrink as human development pressures grow, implying that funding may need to increase over time. The model therefore offers a flexible tool for balancing the SDGs of human development and biodiversity, by predicting the dynamic changes needed in conservation finance as human development proceeds
Modelling the variation of land surface temperature as determinant of risk of heat-related health events
<p>Abstract</p> <p>Background</p> <p>The evaluation of exposure to ambient temperatures in epidemiological studies has generally been based on records from meteorological stations which may not adequately represent local temperature variability. Here we propose a spatially explicit model to estimate local exposure to temperatures of large populations under various meteorological conditions based on satellite and meteorological data.</p> <p>Methods</p> <p>A general linear model was used to estimate surface temperatures using 15 LANDSAT 5 and LANDSAT 7 images for Quebec Province, Canada between 1987 and 2002 and spanning the months of June to August. The images encompassed both rural and urban landscapes and predictors included: meteorological records of temperature and wind speed, distance to major water bodies, Normalized Differential Vegetation Index (NDVI), land cover (built and bare land, water, or vegetation), latitude, longitude, and week of the year.</p> <p>Results</p> <p>The model explained 77% of the variance in surface temperature, accounting for both temporal and spatial variations. The standard error of estimates was 1.42°C. Land cover and NDVI were strong predictors of surface temperature.</p> <p>Conclusions</p> <p>This study suggests that a statistical approach to estimating surface temperature incorporating both spatially explicit satellite data and time-varying meteorological data may be relevant to assessing exposure to heat during the warm season in the Quebec. By allowing the estimation of space- and time-specific surface temperatures, this model may also be used to assess the possible impacts of land use changes under various meteorological conditions. It can be applied to assess heat exposure within a large population and at relatively fine-grained scale. It may be used to evaluate the acute health effect of heat exposure over long time frames. The method proposed here could be replicated in other areas around the globe for which satellite data and meteorological data is available.</p
Geographical Aspects of Recent Trends in Drug-Related Deaths, with a Focus on Intra-National Contextual Variation.
BACKGROUND: Recent worldwide estimates are of 53 million users of opioids annually, and of 585,000 drug-related deaths, of which two thirds are due to opioids. There are considerable international differences in levels of drug death rates and substance abuse. However, there are also considerable variations within countries in drug misuse, overdose rates, and in drug death rates particularly. Wide intra-national variations characterize countries where drug deaths have risen fastest in recent years, such as the US and UK. Drug deaths are an outcome of drug misuse, which can ideally be studied at a relatively low spatial scale (e.g., US counties). The research literature suggests that small area variations in drug deaths to a considerable degree reflect contextual (place-related) factors as well as individual risk factors. METHODS: We consider the role of area social status, social cohesion, segregation, urbanicity, and drug supply in an ecological regression analysis of county differences in drug deaths in the US during 2015-2017. RESULTS: The analysis of US small area data highlights a range of factors which are statistically significant in explaining differences in drug deaths, but with no risk factor having a predominant role. Comparisons with other countries where small area drug mortality data have been analyzed show differences between countries in the impact of different contextual factors, but some common themes. CONCLUSIONS: Intra-national differences in drug-related deaths are considerable, but there are significant research gaps in the evidence base for small area analysis of such deaths
17-09 Assessing the Impact of Air Pollution on Public Health Along Transit Routes
Transportation sources account for a large proportion of the pollutants found in most urban areas. Also, transportation activity and intensity appear likely to contribute to the risk of respiratory disease occurrence. This research investigates the impacts of transportation, urban design and socioeconomic characteristics on the risk of air pollution-related respiratory diseases in two of the biggest MSAs (Metropolitan Statistical Areas) in the US, Dallas-Fort Worth (DFW) and Los Angeles at the block group (BG) level, by considering the US Environmental Protection Agency’s respiratory hazard quotient (RHQ) as the dependent variable. The researchers identify thirty candidate indicators of disease risk from previous studies and use them as independent variables in the model. The study applies a three-step modeling including Principal Component Analysis (PCA), Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) to reach the final model. The results of this study demonstrate strong spatial correlations in the variability in both MSAs which help explain the impact of the indicators such as socioeconomic characteristics, transit access to jobs, and automobile access on the risk of respiratory diseases. The populations living in areas with higher transit access to jobs in urbanized areas and greater automobile access in more rural areas appear more prone to respiratory diseases after controlling for demographic characteristics
- …