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Over the years empirical evidence has shown that traffic enforcement reduces traffic violations, crashes, and casualties. However, less attention has been paid to enforcement coverage across different populations and driver characteristics. The current study develops and explores a method for estimating police enforcement coverage, by comparing the share of drivers across several characteristics who received tickets from automatic speed and red-light cameras - as an objective estimate of offenses committed - to the share of drivers who received tickets through manual police enforcement. Using data from all speeding and red-light tickets issued to Israelis over a period of one and a half years, we found under-enforcement by police officers for female drivers, two-wheeled vehicle drivers (for speeding), and drivers with previous tickets. We found over-enforcement for younger drivers, truck drivers, and two-wheeled vehicle drivers (for red-light offenses). The findings suggest that the method developed in the research is able to identify groups of drivers who are over- or under-enforced. Police authorities can use this information to create evidence-based enforcement policies.

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BACKGROUND: The communities we live in are central to our health. Neighborhood disadvantage is associated with worse physical and mental health and even early mortality, while resident sense of safety and positive neighborhood sentiment has been repeatedly linked to better physical and mental health outcomes. Therefore, understanding where negative neighborhood sentiment and safety are salient concerns can help inform public health interventions and as a result, improve health outcomes. To date, fear of crime and neighborhood sentiment data or indices have largely been based on the administration of time consuming and costly standardized surveys. OBJECTIVE: The current study aims to develop a Neighborhood Sentiment and Safety Index (NSSI) at the census tract level, building on publicly available data repositories, including the US Census and ACS surveys, Data Axle, and ESRI repositories. METHODS: The NSSI was created using Principal Component Analysis. Mineigen and minimum loading values were 1 and 0.3, respectively. Throughout the step-wise PCA process, variables were excluded if their loading value was below 0.3 or if variables loaded into multiple components. RESULTS: The novel index was validated against standardized survey items from a longitudinal cohort study in the Northeastern United States characterizing experiences of (1) Neighborhood Characteristics with a Pearson correlation of -0.34 (p < 0.001) and, (2) Neighborhood Behavior Impact with a Pearson correlation of -0.33 (p < 0.001). It also accurately predicted the Share Care Community Well Being Index (Spearman correlation = 0.46) and the neighborhood deprivation index (NDI) (Spearman correlation = -0.75). SIGNIFICANCE: Our NSSI can serve as a predictor of neighborhood experience where data is either unavailable or too resource consuming to practically implement in planned studies. IMPACT STATEMENT: To date, fear of crime and neighborhood sentiment data or indices have largely been based on the administration of time consuming and costly standardized surveys. The current study aims to develop a Neighborhood Sentiment and Safety Index (NSSI) at the census tract level, building on publicly available data repositories, including the US Census and ACS surveys, Data Axle, and ESRI repositories. The NSSI was validated against four separate measures and can serve as a predictor of neighborhood experience where data is either unavailable or too resource consuming to practically implement in planned studies.

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Traffic crashes take well over a million lives every year and are mainly caused by driver behavior and traffic violations. Drivers' attitudes and beliefs are at the root of whether traffic violations will be committed, making it important to explore what contributes to disobedience of traffic law. Generalized trust is one of the most influential factors in interpersonal behavior but has not yet been studied empirically in the context of driving behavior in general, and traffic violations in specific. Using data from about 30,000 participants from 20 European countries, this study examines the relationship between generalized trust and committing traffic violations while paying attention to differences between countries scoring high and low in individualism. A multilevel mixed-effects logistic regression analysis shows that in countries with high individualism scores, the probability to commit traffic violations increases significantly as generalized trust increases, while the association between generalized trust and traffic violations decreases as the country's individualism level decreases. The findings and their implications are discussed with suggestions for future research.

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The current study tests an innovative public participation process for designing and implementing a tailored traffic enforcement program in minority localities. The quasi-experiment used two matched pairs of randomly selected Israeli Arab localities, where one locality in each pair was randomly assigned to the experimental group and the other to the control group. The intervention's main features were the public participation process and implementation by police of the traffic enforcement program designed during the process. Systematic field observations on 12,236 vehicles in the four localities found a meaningful and significant reduction in traffic violations in the experimental localities following the intervention, while a small increase in violations was observed in the control localities. The most meaningful decline, indicating improvement in drivers' behavior, was in non-use of seatbelts and small children in the front seat. The study suggests that a public participation process which identifies local road traffic problems and "dark" hot spots (places where offenses and risky behavior recur but might not be known to the police), followed by implementing tailored solutions for these problems, can reduce traffic violations. Future research should aim to separate out the independent effects of the two phases (the public participation process and tailored enforcement).

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Noise pollution is a common phenomenon of the 21st century. Noise prediction models tend to estimate noise levels mainly from road traffic sources (such as cars, public transportation etc.). This paper describes the adoption of land use regression (LUR) modeling methodology to assess noise pollution in two periods of the day (rush hour and off-peak), in two major cities in Israel (Tel Aviv and Beer Sheva). For both rush hour and off-peak times, 20 min short term measurements were used to develop a LUR noise estimation model. We used GIS-based predictors alongside commonly used traffic predictors. The findings show good fits for our model, with rush hour "out of sample" ten folds cross-validated R² of 0.79 (Tel Aviv) and 0.52 (Beer Sheva). The Tel Aviv model performance was also tested with independent monitoring data in an adjacent city (Bat Yam), presenting a good performance as well (R² of 0.93). The findings demonstrate the viability of using a LUR approach for applying high-resolution spatial data to estimate and map noise pollution for environmental noise assessment.

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OBJECTIVES: The current study aims to develop a theoretical framework for understanding the antecedents of corruption and the effects of corruption on various health indicators. METHODS: Using structural equation models, we analyzed a multinational dataset of 133 countries that included three main groups of variables--antecedents of corruption, corruption measures, and health indicators. RESULTS: Controlling for various factors, our results suggest that corruption rises as GDP per capita falls and as the regime becomes more autocratic. Higher corruption is associated with lower levels of health expenditure as a percentage of GDP per capita, and with poorer health outcomes. Countries with higher GDP per capita and better education for women have better health outcomes regardless of health expenditures and regime type. CONCLUSIONS: Our results suggest that there is no direct relationship between health expenditures and health outcomes after controlling for the other factors in the model. Our study enhances our understanding of the conceptual and theoretical links between corruption and health outcomes in a population, including factors that may mediate how corruption can affect health outcomes.

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Studies have reported differences in the public's understanding of, trust in, and satisfaction with its priority-setting processes and outcomes across countries. How the media frames and reports decision making processes and outcomes may both reflect and affect the public's knowledge of and attitudes toward them. Nevertheless, no studies have analyzed how priority-setting decision making processes are portrayed in the media. We analyzed 202 newspaper articles published over a decade, from January 2000 through December 2009, in leading newspapers of Israel and South Korea. The findings reveal intriguing differences between the countries in both the number and content of the reports. The issue of priority setting is much less salient in Korean than in Israeli society. While the complexity of the task was the most prevalent theme in the Israeli reports sampled, benefits package expansion decisions were most common in the Korean reports. Similarly, the Israeli reports emphasized the qualifications and backgrounds of individual members of the decision making committee, but the equivalent Korean committee was not portrayed as a major actor, and so received less attention. The least reported theme in both countries was priority-setting procedures and principles. These findings, along with results from previous studies which indicate that public satisfaction with the two systems differs between the countries, provoke several interesting future research questions.

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Road traffic crashes are globally a leading cause of death. The current study tests the effect of traffic tickets issued to drivers on subsequent crashes, using a unique dataset that overcomes some shortcomings of previous studies. The study takes advantage of a national longitudinal dataset at the individual level that merges Israeli census data with data on traffic tickets issued by the police and official data on involvement in road traffic crashes over seven years. The results show that the estimated probability of involvement in a subsequent fatal or severe crash was more than eleven times higher for drivers with six traffic tickets per year compared to those with one ticket per year, while controlling for various confounders. However, the majority of fatal and severe crashes involved the larger population of drivers who received up to one ticket on average per year. The current findings indicate that reducing traffic violations may contribute significantly to crash and injury reduction. In addition, mass random enforcement programs may be more effective in reducing fatal and severe crashes than targeting high-risk recidivist drivers.

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Previous studies have shown that a community's socioeconomic status has a significant impact on its residents' health, and that vulnerability in deprived populations expresses itself as variability in health outcomes. The current study adds to this ecological research approach the notion that underlying community vulnerabilities are also related to the physical environment and population growth of a locality. The paper explores the variability in various health indicators in 252 localities in Israel as a function of the localities' socioeconomic status, population growth, and land use composition measures. The results indicate that a locality's socioeconomic status and its land use composition are both strongly associated with various health outcomes and their variability. These findings are of particular interest in light of the fact that the results were obtained from a country with a universal healthcare system.

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OBJECTIVES: The recently developed social resistance framework addresses a widespread pattern in which members of some nondominant minorities tend to engage in various risky and unhealthy behaviors more than the majority group. This pilot study tested the core hypotheses derived from this innovative framework. METHODS: We conducted in 2011 a nationally representative Web-based survey of 200 members of a nondominant minority group (African Americans) and 200 members of a majority group (Whites). RESULTS: The preliminary findings supported the main premises of the framework and suggested that nondominant minorities who felt discriminated and alienated from society tended also to have higher levels of social resistance. Those with higher levels of social resistance also engaged more in risky and unhealthy behaviors-smoking, drinking, and nonuse of seat belts-than did those with lower levels of social resistance. These associations were not found in the majority group. CONCLUSIONS: These preliminary results supported the framework and suggested that social resistance might play a meaningful role in risky and unhealthy behaviors of nondominant minorities, and should be taken into account when trying to reduce health disparities.

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BACKGROUND: The recently developed social resistance framework addresses a widespread pattern whereby non-dominant minority groups, such as ethnic/racial minorities and people of low socioeconomic status, often engage in unhealthy and risky behaviours at higher rates compared with society at large. The framework suggests that power relations within society may encourage members of non-dominant minority groups to actively engage in acts of everyday resistance, which may include risky and unhealthy behaviours. METHODS: The current paper develops and psychometrically evaluates a research tool to test this innovative framework. The UNREST questionnaire measures the key concepts of the framework, along with four high-risk and unhealthy behaviours, as well as demographic and socioeconomic characteristics. A pilot survey was conducted among representative subsamples of a non-dominant group (African-Americans) and a dominant group (Caucasians). RESULTS: Consistent with the general premises of the framework, the evaluation of the questionnaire produced six valid and reliable scales, which were significantly correlated with some criterion-related items as well as unhealthy and risky behaviours. CONCLUSIONS: The preliminary results of our pilot study suggest that the new tool may be useful for testing the framework. The results also provide support for the framework in general.

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INTRODUCTION: Although prior studies of road traffic accidents have found between-group differences in risk, little attention has been given to the encounter between drivers involved in severe collisions. METHOD: The present study empirically evaluates two different possible causes of "social accidents," which are defined as collisions between two or more drivers where some faulty social interaction might be assumed, and which are the most prevalent cause of road injuries. The analyses use merged Israeli collision records from 1983 to 2004 with data from two national censuses, thus providing an unprecedented empirical basis to study the social foundations of car accidents. The data are used to adjudicate between two alternative hypotheses: the heterogeneity hypothesis (socially different drivers tend to collide) versus the homogeneity hypothesis (socially similar drivers tend to collide). RESULTS: Multivariate analyses provide preliminary support for the latter hypothesis. Given an accident, there are more collisions among drivers from the same broad educational group, and the factors that influence this correlation are independent of geography. The paper thus leads to the idea that severe collisions reflect a sociological or ecological process that is akin to acciphilia. IMPACT ON INDUSTRY: The preliminary findings suggest that variation between drivers may be preferable to similarity, since apparently there is a greater tendency toward collisions between similar drivers.

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Across different societies, non-dominant minority groups, compared to the dominant group, often exhibit higher rates of involvement in high-risk behaviors, such as smoking, drug and alcohol use, sexual risk behaviors, overeating, and unsafe driving habits. In turn, these behaviors have a well-documented impact on chronic disease, morbidity, and mortality. Previous studies have emphasized macro-structural or micro-agentic explanations for this phenomenon. Such explanations suffer from mirror-image shortcomings, such as, by emphasizing structural barriers, macro-level explanations leave out individual agency ("the over-socialized conception of the individual"), while micro-level theories give short shrift to structural constraints that prevent individuals from engaging in health-promoting behaviors ("the under-socialized conception of the individual"). Moreover, most current theories regard individuals as passive players who are influenced by the social environment or by psychological problems, or who make "bad" choices. The current paper develops an integrated theoretical framework that incorporates structural inequalities while leaving intact the role of individual agency. According to the social resistance framework, power relations in society encourage members of non-dominant minority groups to actively engage in everyday resistance practices that include various unhealthy behaviors. The paper develops propositions from which testable hypotheses can be generated, and discusses the implications and contributions of the social resistance framework.

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Prior studies in the sociology of accidents have shown that different social groups have different rates of accident involvement. This study extends those studies by implementing Bourdieu's relational perspective of social space to systematically explore the homology between drivers' social characteristics and their involvement in specific types of motor vehicle accident. Using a large database that merges official Israeli road-accident records with socioeconomic data from two censuses, this research maps the social order of road accidents through multiple correspondence analysis. Extending prior studies, the results show that different social groups indeed tend to be involved in motor vehicle accidents of different types and severity. For example, we find that drivers from low socioeconomic backgrounds are overinvolved in severe accidents with fatal outcomes. The new findings reported here shed light on the social regularity of road accidents and expose new facets in the social organization of death.

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This paper assesses group differences in severe and fatal road-traffic accidents by using a unique database that merges road-traffic records with the Israeli census data. The database traces, over a period of 9 years, a group of drivers that comprises 20% of the Israeli population and explores the probability of their being involved in an accident. This unique database enables the investigation of drivers' socioeconomic and demographic characteristics, while controlling for a variety of variables, such as estimated daily distance traveled and license type. Testing a previously published theoretical paper on the social bases of accidents, the findings expose significant group differences in estimated probabilities of being involved in severe and fatal accidents. For example, estimated probabilities of accident involvement are higher for males than for females, for non-Jewish drivers than for Jewish, and for drivers whose origins are in Africa and Asia than in America and Europe. Furthermore, the higher one's education and socioeconomic status, the lower is the probability of accident-involvement. The implications of the findings for developing road-safety programs and suggestions for future research are discussed.

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The paper develops a sociological model to explain collisions between two drivers or more. The "Social Accident" model presented here integrates empirical findings from prior studies and extant sociological theories. Sociological theory posits that social groups have unique cultural characteristics, which include a distinctive world view and ways of operating that influence its members. These cultural characteristics may cause drivers in different groups to interpret a given situation differently; therefore, they will make conflicting decisions that may possibly lead to road accidents. The proposed model may contribute to an understanding of the social mechanism related to interactions and communication among drivers by presenting new directions for understanding accidents and collisions. The paper concludes with suggestions for future research that will employ the model to assess its predictive and practical utility.

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