Polymorphisms in glutathione S-transferases and non-melanoma skin cancer risk in Australian renal transplant recipients

Caucasian renal transplant recipients from Queensland, Australia have the highest non-melanoma skin cancer (NMSC) risk worldwide. Although ultraviolet light (UVR) exposure is critical, genetic factors also appear important. We and others have shown that polymorphism in the glutathione S-transferases (GST) is associated with NMSC in UK recipients. However, the effect of high UVR exposure and differences in immunosuppressive regimen on these associations is unknown. In this study, we examined allelism in GSTM1, GSTM3, GSTT1 and GSTP1 in 361 Queensland renal transplant recipients. Data on squamous (SCC) and basal cell carcinoma (BCC), UVR/tobacco exposure and genotype were obtained. Associations with both NMSC risk and numbers were examined using logistic and negative binomial regression, respectively. In the total group, GSTM1 AB [P = 0.049, rate ratio (RR) = 0.23] and GSTM3 AA (P = 0.015, RR = 0.50) were associated with fewer SCC. Recipients were then stratified by prednisolone dose (less than or equal to7 versus >7 mg/day). In the low-dose group, GSTT1 null (P = 0.006, RR = 0.20) and GSTP1 Val/Val (P = 0.021, RR = 0.20) were associated with SCC numbers. In contrast, in the high-dose group, GSTM1 AB (P = 0.009, RR = 0.05), GSTM3 AB (P = 0.042, RR = 2.29) and BB (P = 0.014, RR = 5.31) and GSTP1 Val/Val (P = 0.036, RR = 2.98) were associated with SCC numbers. GSTM1 AB (P = 0.016) and GSTP1 Val/Val (P = 0.046) were also associated with fewer BCC in this group. GSTP1 associations were strongest in recipients with lower UVR/tobacco exposure. The data confirm our UK findings, suggesting that protection against UVR-induced oxidative stress is important in NMSC development in recipients, but that this effect depends on the immunosuppressant regimen.

Food intake and risk of squamous cell carcinoma of the skin in a community: The Nambour skin cancer cohort study

There is some evidence that dietary factors may modify the risk of squamous cell carcinoma (SCC) of the skin, but the association between food intake and SCC has not been evaluated prospectively. We examined the association between food intake and SCC incidence among 1,056 randomly selected adults living in an Australian sub-tropical community. Measurement-error corrected estimates of intake in 15 food groups were defined from a validated food frequency questionnaire in 1992. Associations with SCC risk were assessed using Poisson and negative binomial regression to the persons affected and tumour counts, respectively, based on incident, histologically confirmed tumours occurring between 1992 and 2002. After multivariable adjustment, none of the food groups was significantly associated with SCC risk. Stratified analysis in participants with a past history of skin cancer showed a decreased risk of SCC tumours for high intakes of green leafy vegetables (RR = 0.45, 95% CI = 0.22-0.91; p for trend = 0.02) and an increased risk for high intake of unmodified dairy products (RR = 2.53, 95% CI: 1.15-5.54; p for trend = 0.03). Food intake was not associated with SCC risk in persons who had no past history of skin cancer. These findings suggest that consumption of green leafy vegetables may help prevent development of subsequent SCCs of the skin among people with previous skin cancer and that consumption of unmodified dairy products, such as whole milk, cheese and yoghurt, may increase SCC risk in susceptible persons.

Development of safety performance functions for SafetyAnalyst applications in Florida

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency's safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs. In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from 2007-2010 for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models. The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors. The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests, represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R2 (R2FT), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibrated to Florida data. The performance of Florida-specific SPFs was further compared with that of the full SPFs, which include both traffic and geometric variables, in two major applications of SPFs, i.e., crash prediction and identification of high crash locations. The results showed that both SPF models yielded very similar performance in both applications. These empirical results support the use of the flow-only SPF models adopted in SafetyAnalyst, which require much less effort to develop compared to full SPFs.

Improved Criteria for Estimating Calibration Factors for Highway Safety Manual (HSM) Applications

The Highway Safety Manual (HSM) estimates roadway safety performance based on predictive models that were calibrated using national data. Calibration factors are then used to adjust these predictive models to local conditions for local applications. The HSM recommends that local calibration factors be estimated using 30 to 50 randomly selected sites that experienced at least a total of 100 crashes per year. It also recommends that the factors be updated every two to three years, preferably on an annual basis. However, these recommendations are primarily based on expert opinions rather than data-driven research findings. Furthermore, most agencies do not have data for many of the input variables recommended in the HSM. This dissertation is aimed at determining the best way to meet three major data needs affecting the estimation of calibration factors: (1) the required minimum sample sizes for different roadway facilities, (2) the required frequency for calibration factor updates, and (3) the influential variables affecting calibration factors. In this dissertation, statewide segment and intersection data were first collected for most of the HSM recommended calibration variables using a Google Maps application. In addition, eight years (2005-2012) of traffic and crash data were retrieved from existing databases from the Florida Department of Transportation. With these data, the effect of sample size criterion on calibration factor estimates was first studied using a sensitivity analysis. The results showed that the minimum sample sizes not only vary across different roadway facilities, but they are also significantly higher than those recommended in the HSM. In addition, results from paired sample t-tests showed that calibration factors in Florida need to be updated annually. To identify influential variables affecting the calibration factors for roadway segments, the variables were prioritized by combining the results from three different methods: negative binomial regression, random forests, and boosted regression trees. Only a few variables were found to explain most of the variation in the crash data. Traffic volume was consistently found to be the most influential. In addition, roadside object density, major and minor commercial driveway densities, and minor residential driveway density were also identified as influential variables.

UPTAKE OF THE PERIODIC HEALTH EXAM AMONG ADULTS WITH INTELLECTUAL AND DEVELOPMENTAL DISABILITIES FROM 2003 TO 2015

Background: Over the past decade, annual heath exams have been de-emphasized for the general population but emphasized for adults with intellectual and developmental disabilities (IDD). The purpose of this project was to determine if there has been an increase in the uptake of the health exam among adults with IDD in Ontario, to what extent, and the effect on the quality of preventive care provided. Methods: Using administrative health data, the proportion of adults (18-64 years old) with IDD who received a health exam (long appointment, general assessment, and “true” health exam), a high value on the primary care quality composite score (PCQS), and a health exam or high PCQS each year was compared to the proportion in a propensity score matched sample of the general population. Negative binomial and segmented negative binomial regression controlling for age and sex were used to determine the relative risk of having a health exam/high PCQS/health exam or PCQS over time. Results: Pre joinpoint, the long appointment and general assessment health exam definitions saw a decrease and the “true” health exam saw an increase in the likelihood of adults having a health exam. Post joinpoint, all health exam definitions saw a decrease in the likelihood of adults having a health exam. Pre joinpoint, all PCQS measures (high PCQS, long appointment or high PCQS, “true” health exam or high PCQS) saw an increase in the likelihood for adults to achieve a high PCQS or high PCQS/have a health exam. Post joinpoint, all PCQS measures saw a decrease in the likelihood for adults to achieve a high PCQS or high PCQS/have a health exam. Achieving a high PCQS was strongly associated with having a health exam regardless of health exam definition or IDD status. Conclusions: Despite the publication of guidelines, only a small proportion of adults with IDD are receiving health exams. This indicates that the publication of guidelines alone was not sufficient to change practice. More targeted measures, such as the implementation of an IDD-specific health exam fee code, should be considered to increase the uptake of the health exam among adults with IDD.

Estimación del descarte y captura incidental en la pesquería industrial de cerco del stock norte-centro de la anchoveta peruana (Engraulis ringens)

Este estudio presenta la validación de las observaciones que realizó el programa de observación pesquera llamado Programa Bitácoras de Pesca (PBP) durante el periodo 2005 - 2011 en el área de distribución donde operan las embarcaciones industriales de cerco dedicadas a la pesca del stock norte-centro de la anchoveta peruana (Engraulis ringens). Además, durante ese mismo periodo y área de distribución, se estimó la magnitud del descarte por exceso de captura, descarte de juveniles y la captura incidental de dicha pesquera. Se observaron 3 768 viajes de un total de 302 859, representando un porcentaje de 1.2 %. Los datos del descarte por exceso de captura, descarte de juveniles y captura incidental registrados en los viajes observados, se caracterizaron por presentar un alta proporción de ceros. Para la validación de las observaciones, se realizó un estudio de simulación basado en la metodología de Monte Carlo usando un modelo de distribución binomial negativo. Esta permite inferir sobre el nivel de cobertura óptima y conocer si la información obtenida en el programa de observación es contable. De este análisis, se concluye que los niveles de observación actual se deberían incrementar hasta tener un nivel de cobertura de al menos el 10% del total de viajes que realicen en el año las embarcaciones industriales de cerco dedicadas a la pesca del stock norte-centro de la anchoveta peruana. La estimación del descarte por exceso de captura, descarte de juveniles y captura incidental se realizó mediante tres metodologías: Bootstrap, Modelo General Lineal (GLM) y Modelo Delta. Cada metodología estimó distintas magnitudes con tendencias similares. Las magnitudes estimadas fueron comparadas usando un ANOVA Bayesiano, la cual muestra que hubo escasa evidencia que las magnitudes estimadas del descarte por exceso de captura por metodología sean diferentes, lo mismo se presentó para el caso de la captura incidental, mientras que para el descarte de juveniles mostró que hubieron diferencias sustanciales de ser diferentes. La metodología que cumplió los supuestos y explico la mayor variabilidad de las variables modeladas fue el Modelo Delta, el cual parece ser una mejor alternativa para la estimación, debido a la alta proporción de ceros en los datos. Las estimaciones promedio del descarte por exceso de captura, descarte de juveniles y captura incidental aplicando el Modelo Delta, fueron 252 580, 41 772, 44 823 toneladas respectivamente, que en conjunto representaron el 5.74% de los desembarques. Además, con la magnitud de la estimación del descarte de juveniles, se realizó un ejercicio de proyección de biomasa bajo el escenario hipotético de no mortalidad por pesca y que los individuos juveniles descartados sólo presentaron tallas de 8 y 11 cm., en la cual se obtuvo que la biomasa que no estará disponible a la pesca está entre los 52 mil y 93 mil toneladas.

Upgrading Bridge Rails on Low-Volume Roads in Iowa, TR-679, 2016

Building on previous research, the goal of this project was to identify significant influencing factors for the Iowa Department of Transportation (DOT) to consider in future updates of its Instructional Memorandum (I.M.) 3.213, which provides guidelines for determining the need for traffic barriers (guardrail and bridge rail) at secondary roadway bridges—specifically, factors that might be significant for the bridge rail rating system component of I.M. 3.213. A literature review was conducted of policies and guidelines in other states and, specifically, of studies related to traffic barrier safety countermeasures at bridges in several states. In addition, a safety impact study was conducted to evaluate possible non-driver-related behavior characteristics of crashes on secondary road structures in Iowa using road data, structure data, and crash data from 2004 to 2013. Statistical models (negative binomial regression) were used to determine which factors were significant in terms of crash volume and crash severity. The study found that crashes are somewhat more frequent on or at bridges possessing certain characteristics—traffic volume greater than 400 vehicles per day (vpd) (paved) or greater than 50 vpd (unpaved), bridge length greater than 150 ft (paved) or greater than 35 ft (unpaved), bridge width narrower than its approach (paved) or narrower than 20 ft (unpaved), and bridges older than 25 years (both paved and unpaved). No specific roadway or bridge characteristic was found to contribute to more serious crashes. The study also confirmed previous research findings that crashes with bridges on secondary roads are rare, low-severity events. Although the findings of the study support the need for appropriate use of bridge rails, it concludes that prescriptive guidelines for bridge rail use on secondary roads may not be necessary, given the limited crash expectancy and lack of differences in crash expectancy among the various combinations of explanatory characteristics.

Development of Safety Performance Functions for SafetyAnalyst Applications in Florida

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency’s safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs. In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from 2007-2010 for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models. The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors. The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests, represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R2 (R2FT), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibrated to Florida data. The performance of Florida-specific SPFs was further compared with that of the full SPFs, which include both traffic and geometric variables, in two major applications of SPFs, i.e., crash prediction and identification of high crash locations. The results showed that both SPF models yielded very similar performance in both applications. These empirical results support the use of the flow-only SPF models adopted in SafetyAnalyst, which require much less effort to develop compared to full SPFs.

Empirical Bayes method in the study of traffic safety via heterogeneous negative multinomial model

In the study of traffic safety, expected crash frequencies across sites are generally estimated via the negative binomial model, assuming time invariant safety. Since the time invariant safety assumption may be invalid, Hauer (1997) proposed a modified empirical Bayes (EB) method. Despite the modification, no attempts have been made to examine the generalisable form of the marginal distribution resulting from the modified EB framework. Because the hyper-parameters needed to apply the modified EB method are not readily available, an assessment is lacking on how accurately the modified EB method estimates safety in the presence of the time variant safety and regression-to-the-mean (RTM) effects. This study derives the closed form marginal distribution, and reveals that the marginal distribution in the modified EB method is equivalent to the negative multinomial (NM) distribution, which is essentially the same as the likelihood function used in the random effects Poisson model. As a result, this study shows that the gamma posterior distribution from the multivariate Poisson-gamma mixture can be estimated using the NM model or the random effects Poisson model. This study also shows that the estimation errors from the modified EB method are systematically smaller than those from the comparison group method by simultaneously accounting for the RTM and time variant safety effects. Hence, the modified EB method via the NM model is a generalisable method for estimating safety in the presence of the time variant safety and the RTM effects.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

A Poisson mixed model with nonnormal random effect distribution

In this paper, we propose a random intercept Poisson model in which the random effect is assumed to follow a generalized log-gamma (GLG) distribution. This random effect accommodates (or captures) the overdispersion in the counts and induces within-cluster correlation. We derive the first two moments for the marginal distribution as well as the intraclass correlation. Even though numerical integration methods are, in general, required for deriving the marginal models, we obtain the multivariate negative binomial model from a particular parameter setting of the hierarchical model. An iterative process is derived for obtaining the maximum likelihood estimates for the parameters in the multivariate negative binomial model. Residual analysis is proposed and two applications with real data are given for illustration. (C) 2011 Elsevier B.V. All rights reserved.

Accident prediction model for railway-highway interfaces

Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes. Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes.

Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

Modeling random effect and excess zeros in road traffic accident prediction

Poisson distribution has often been used for count like accident data. Negative Binomial (NB) distribution has been adopted in the count data to take care of the over-dispersion problem. However, Poisson and NB distributions are incapable of taking into account some unobserved heterogeneities due to spatial and temporal effects of accident data. To overcome this problem, Random Effect models have been developed. Again another challenge with existing traffic accident prediction models is the distribution of excess zero accident observations in some accident data. Although Zero-Inflated Poisson (ZIP) model is capable of handling the dual-state system in accident data with excess zero observations, it does not accommodate the within-location correlation and between-location correlation heterogeneities which are the basic motivations for the need of the Random Effect models. This paper proposes an effective way of fitting ZIP model with location specific random effects and for model calibration and assessment the Bayesian analysis is recommended.

Investigating pedestrian injury crashes on modern roundabouts in Addis Ababa, Ethiopia

Pedestrian crashes are one of the major road safety problems in developing countries representing about 40% of total fatal crashes in low income countries. Despite the fact that many pedestrian crashes in these countries occur at unsignalized intersections such as roundabouts, studies focussing on this issue are limited—thus representing a critical research gap. The objective of this study is to develop safety performance functions for pedestrian crashes at modern roundabouts to identify significant roadway geometric, traffic and land use characteristics related to pedestrian safety. To establish the relationship between pedestrian crashes and various causal factors, detailed data including various forms of exposure, geometric and traffic characteristics, and spatial factors such as proximity to schools and proximity to drinking establishments were collected from a sample of 22 modern roundabouts in Addis Ababa, Ethiopia, representing about 56% of such roundabouts in Addis Ababa. To account for spatial correlation resulting from multiple observations at a roundabout, both the random effect Poisson (REP) and random effect Negative Binomial (RENB) regression models were estimated and compared. Model goodness of fit statistics reveal a marginally superior fit of the REP model compared to the RENB model of pedestrian crashes at roundabouts. Pedestrian crossing volume and the product of traffic volumes along major and minor road had significant and positive associations with pedestrian crashes at roundabouts. The presence of a public transport (bus/taxi) terminal beside a roundabout is associated with increased pedestrian crashes. While the maximum gradient of an approach road is negatively associated with pedestrian safety, the provision of a raised median along an approach appears to increase pedestrian safety at roundabouts. Remedial measures are identified for combating pedestrian safety problems at roundabouts in the context of a developing country.