MACROLEV - I Have To Know !LINK!
Methods. We used meta-regressions to describe ecological associations between macrolevel factors and mean BMIs across countries. Multilevel regression was used to assess the relation between macrolevel economic characteristics and individual odds of underweight and overweight relative to normal weight.
MACROLEV - I Have To Know
Despite the potential role that these macrolevel economic factors may play in shaping the epidemiological pattern of diet, behavior, and weight in LMICs, few empirical studies have investigated the relation between contextual factors and individual weight. A limited number of ecological studies have been conducted,9,22 but their results cannot be used to draw inferences about health at the individual level. Furthermore, the social patterning of diet and physical activity according to area of residence (urban or rural) and gender suggests that the macrolevel factors posited to drive changes in weight may have distinct implications for particular groups of individuals,23,24 and ecological studies cannot assess whether associations between macrolevel economic characteristics and weight vary according to such individual-level characteristics.
We calculated country-specific mean BMIs for men and women and used random effects meta-regression analyses to assess the unadjusted ecological associations between macrolevel factors and male and female mean BMIs across countries. These models, run via the Metafor package in R,44 were weighted by the inverse of the standard error of the gender-specific mean BMI for each country.
Nonetheless, few studies have considered whether macrolevel exposures, particularly those related to neoliberalization and greater market integration, are associated with variations in nutritional outcomes such as weight status. Consistent with prior ecological work,9,22 the results of our meta-regression analyses showed that levels of economic development and urbanization were positively associated with mean BMIs across countries. Our results also showed that average BMIs were higher in countries with greater FDI and fewer barriers to trade.
To assess whether macrolevel factors were related to individual weight status independently of compositional characteristics, we used multilevel regression models that controlled for country- and individual-level covariates. Our findings showed that macrolevel factors were associated with nutritional status variations in LMICs. Higher levels of urbanization and trade liberalization were associated with lower odds of underweight relative to normal weight among both urban and rural men and women. We corroborated previous findings17,18 by showing that economic development was associated with higher odds of overweight relative to normal weight. In addition, FDI was positively associated with the odds of overweight relative to normal weight among rural men, providing preliminary evidence that regulatory environments facilitating investments by foreign companies may adversely affect nutritional status in LMICs.
The extent to which the relatively immediate mental health effects of September 11 revealed in most studies have lingered is just beginning to be addressed. Boscarino et al.23 found that exposure to psychological trauma related to the World Trade Center attack in New York City was associated with increased alcohol consumption 2 years after the attack. Richman et al.24 demonstrated that a substantial percentage of a Midwestern population maintained negative beliefs and fears about their future safety linked to threats of future terrorist attacks in 2003 and that, after they controled for distress and drinking before September 11, these beliefs were significantly associated with distress and problematic drinking. However, a major limitation of that study was that terrorism-related beliefs and fears were measured at the same time point as distress and drinking outcomes, and thus the causal direction of the relationship between terrorism-related fears and mental health was ambiguous.
The final wave-1 sample comprised 2492 employees (52% response rate). The lower than desired response rate reflected the fact that individuals may have been reluctant to complete questionnaires that were self-administered and contained identifiers for subsequent tracking and highly sensitive material.26 A comparison of the characteristics of the sample with the characteristics of the overall employee population indicated no significant differences in terms of race within each occupational stratum. Gender differences between our sample and the overall population were also very small and non-significant for 2 of the 4 strata (service workers and student trainees). However, men were overrepresented by 8.3 percentage points within the clerical group, and women were overrepresented by 11.3 percentage points in the faculty group.27
Another limitation is that the measure assessing negative terrorism-related beliefs and fears may have tapped other period effects, including the war in Iraq, economic conditions, and the general policies of the Bush administration, that have covaried with terrorism-related issues. Finally, it may be useful in future studies to disaggregate the overall negative terrorism-related beliefs measure as a means of addressing the relative salience of different subcomponents with respect to mental health assessment.
In 2013, 1.25 million people were killed by the road traffic crashes worldwide and more than 50 million were injured [1]. Moreover, casualty rates caused by traffic crashes were significantly higher in the low or middle-income countries than that in the high-income countries. Taking China as an example, traffic crashes caused 58,523 deaths and 211,882 injuries in 2014[2]. In the same period, the crashes caused 32,744 deaths and 2,338 thousand injuries in 2014 in the United States [3]. With the rapid growth of economic development and autoownership, traffic crashes have become a leading cause of mortality in many developing countries, which attracted increasing attention from both the government and the public. Thus, it has become increasingly necessary for all the countries in the world to put considerable efforts to enhance the road safety, particularly in the developing countries.
The microlevel research focuses on the specific influencing factors of traffic crashes and casualties in the field of traffic safety research. The purpose of the microlevel research is to propose targeted measures to improve the vehicle, road, and environment. It is easy to understand the correlation between these direct contributing factors and traffic crashes. However, from another perspective, the macrolevel research focuses on the relationship between traffic crashes and society, economy, and environment. Compared with the microlevel safety research, the macrolevel safety analysis can identify safety problems more effectively in a larger area, which is more useful in helping establish a long-term planning policy to improve the traffic safety [4].
More importantly, with the continual development of data mining technology, open source data has raised more and more attention in recent years. The point-of-interest (POI) data are the more specific data of land use factors with exact information of location which are supposed to be highly related to the user characteristics and traffic crashes in both macro- and microaspects [5]. A POI database can be applied to describe the specific influence factors which are spatially correlated to the distribution of macrolevel traffic crashes. This study focuses on the spatial autocorrelation between the crashes and the impact of the different types of POI densities on the occurrences of crashes in the target units and adjacent units. The purpose of this paper is twofold: (a) to investigate the optimal spatial econometric model and (b) to evaluate the spatial direct effect and spillover effects of contributory factors that related to traffic crashes by using the POI dataset.
As collisions are believed to be discrete, nonnegative, and random, most of the previous literature related to the collision models is accountable for the Poisson regression models. Poisson model requires that the variance of data be equal to the mean, which is difficult to achieve in practice. Therefore, Poisson lognormal (PLN) model and Negative Binomial (NB) regression model are proposed to overcome these shortcomings [13]. However, the commonly used PLN model and NB model assume that the distribution of crashes is independent in space, while the crashes data have the spatial correlation characteristics in reality.
This study focuses on the macrolevel traffic crashes using the spatial econometric model in TAZs level. The POI data has been used to estimate the spatial spillover effects of traffic crashes influencing factors. In summary, we believe that by distinguishing the vital POI features and quantitative analyzing spatial spillover effects on the occurrence of traffic crashes we can contribute some recommendations to improve safety through traffic control policy management.
where Y denotes a dependent variable matrix, X denotes an explanatory variable matrix, WY denotes the endogenous interaction effects among the dependent variable, WX denotes the exogenous interaction effects among the independent variables, and Wu refers to the interaction effects among the disturbance term of the different units. ln is a vector of ones associated with the constant term parameter α to be estimated, ρ is called the spatial autoregressive coefficient, λ is the spatial autocorrelation coefficient, θ, β are vectors with unknown parameters to be estimated, and ε is a vector of disturbance errors.
At the same time, the outcomes indicate that the coefficient ρ/λ connected with spatial autocorrelation is positive and significantly different from zero for all spatial estimations. It shows that the dependent variable in neighboring TAZs has significantly influenced the crash frequency. More importantly, the spatial model has been found to have a better interpretation than the traditional OLS model. The SAR model has the highest value of log-likelihood and the lowest value of AIC and SIC. Hence, the results indicate that it is necessary to consider spatial factors while building a crash model, and it has been proved statically that the SAR model would be a better approach than the SEM model. 041b061a72