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INTRODUCTION: The First UN Decade of Action for Road Safety (2011-2020) ended with most low/middle-income countries (LMICs) failing to reduce road traffic deaths. In contrast, Brazil reported a strong decline starting in 2012. However, comparisons with global health statistical estimates suggest that official statistics from Brazil under-report traffic deaths and overestimate declines. Therefore, we sought to assess the quality of official reporting in Brazil and explain discrepancies. METHODS: We obtained national death registration data and classified deaths to road traffic deaths and partially specified causes that could include traffic deaths. We adjusted data for completeness and reattributed partially specified causes proportionately over specified causes. We compared our estimates with reported statistics and estimates from the Global Burden of Disease (GBD)-2019 study and other sources. RESULTS: We estimate that road traffic deaths in 2019 exceeded the official figure by 31%, similar to traffic insurance claims (27.5%) but less than GBD-2019 estimates (46%). We estimate that traffic deaths have declined by 25% since 2012, close to the decline estimated by official statistics (27%) but much more than estimated by GBD-2019 (10%). We show that GBD-2019 underestimates the extent of recent improvements because GBD models do not track the trends evident in the underlying data. CONCLUSION: Brazil has made remarkable progress in reducing road traffic deaths in the last decade. A high-level evaluation of what has worked in Brazil could provide important guidance to other LMICs.
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BACKGROUND: There are large discrepancies between official statistics of traffic injuries in African countries and estimates from the Global Burden of Disease (GBD) study and WHO's Global Status Reports on Road Safety (GSRRS). We sought to assess the magnitude of the discrepancy in Ethiopia, its implications and how it can be addressed. METHODS: We systematically searched for nationally representative epidemiological data sources for road traffic injuries and vehicle ownership in Ethiopia and compared estimates with those from GBD and GSRRS. FINDINGS: GBD and GSRRS estimates vary substantially across revisions and across projects. GSRRS-2018 estimates of deaths (27 326 in 2016) are more than three times GBD-2019 estimates (8718), and these estimates have non-overlapping uncertainty ranges. GSRRS estimates align well with the 2016 Demographic and Health Survey (DHS-2016; 27 838 deaths, 95th CI: 15 938 to 39 738). Official statistics are much lower (5118 deaths in 2018) than all estimates. GBD-2019 estimates of serious non-fatal injuries are consistent with DHS-2016 estimates (106 050 injuries, 95th CI: 81 728 to 130 372) and older estimates from the 2003 World Health Survey. Data from five surveys confirm that vehicle ownership levels in Ethiopia are much lower than in other countries in the region. INTERPRETATION: Inclusion of data from national health surveys in GBD and GSRRS can help reduce discrepancies in estimates of deaths and support their use in highlighting under-reporting in official statistics and advocating for better prioritisation of road safety in the national policy agenda. GBD methods for estimating serious non-fatal injuries should be strengthened to allow monitoring progress towards Sustainable Development Goal target 3.6.
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Acidentes de Trânsito , Carga Global da Doença , Humanos , Etiópia/epidemiologia , Saúde GlobalRESUMO
INTRODUCTION: There is considerable uncertainty in estimates of traffic deaths in many sub-Saharan African countries, with the Global Burden of Disease (GBD) and the Global Status Report on Road Safety (GSRRS) reporting widely differing estimates. As a case study, we reviewed and compared estimates for Tanzania. METHODS: We estimated the incidence of traffic deaths and vehicle ownership in Tanzania from nationally representative surveys. We compared findings with GBD and GSRRS estimates. RESULTS: Traffic death estimates based on the 2012 census (9382 deaths; 95% CI: 7565 to 11 199) and the 2011-2014 Sample Vital Registration with Verbal Autopsy (8778; 95% CI: 7631 to 9925) were consistent with each other and were about halfway between GBD (5 608; 95% UI: 4506 to 7014) and WHO (16 252; 95% CI: 13 130 to 19 374) estimates and more than twice official statistics (3885 deaths in 2013). Surveys and vehicle registrations data show that motorcycles have increased rapidly since 2007 and now comprise 66% of vehicles. However, these trends are not reflected in GBD estimates of motorcycles in the country, likely resulting in an underestimation of motorcyclist deaths. CONCLUSION: Reducing discrepancies between GBD and GSRRS estimates and demonstrating consistency with local epidemiological data will increase the legitimacy of such estimates among national stakeholders. GBD, which is the only project that models the road-user distribution of traffic deaths in all countries, likely severely underestimates motorcycle deaths in countries where there has been a recent increase in motorcycles. Addressing police under-reporting and strengthening surveillance capacity in Tanzania will allow a better understanding of the road safety problem and better targeting of interventions.
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Acidentes de Trânsito , Saúde Global , Carga Global da Doença , Humanos , Motocicletas , Tanzânia/epidemiologiaRESUMO
INTRODUCTION: Timely, accurate and detailed information about traffic injuries are essential for managing national road safety programmes. However, there is considerable under-reporting in official statistics of many low and middle-income countries (LMICs) and large discrepancies between estimates from the Global Burden of Disease (GBD) study and WHO's Global Health Estimates (GHE). We compared all sources of epidemiological information on traffic injuries in Cambodia to guide efforts to improve traffic injury statistics. METHODS: We estimated the incidence of traffic deaths and injuries and household ownership of motor vehicles in Cambodia from nationally representative surveys and censuses. We compared findings with GDB and GHE estimates. RESULTS: We identified seven sources for estimating traffic deaths and three for non-fatal injuries that are not included as data sources in GBD and GHE models. These sources and models suggest a fairly consistent estimate of approximately 3100 deaths annually, about 50% higher than official statistics, likely because most hospital deaths are not recorded. Surveys strongly suggest that the vehicle fleet is dominated by motorcycles, which is not consistent with GBD estimates that suggest similar numbers of motorcyclist and vehicle occupant deaths. Estimates of non-fatal injuries from health surveys were about 7.5 times official statistics and 1.5 times GBD estimates. CONCLUSION: Including local epidemiological data sources from LMICs can help reduce uncertainty in estimates from global statistical models and build trust in estimates among local stakeholders. Such analysis should be used as a benchmark to assess and strengthen the completeness of reporting of the national surveillance system.
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Acidentes de Trânsito , Ferimentos e Lesões , Camboja/epidemiologia , Carga Global da Doença , Humanos , Veículos Automotores , Motocicletas , Ferimentos e Lesões/epidemiologiaRESUMO
INTRODUCTION: Road crash fatalities have increased significantly in Low- and Middle- Income Countries (LMICs) between 2006 and 2016. This study presents how road safety characteristics have changed in LMICs by comparing data over time and relationships between the road crash fatality increase and a wide range of data from LMICs. Parametric and nonparametric methods are used to test significance. METHOD: There were 35 countries in the Latin America and Caribbean region, the Sub-Saharan Africa region, the East Asia and Pacific region, and the South Asia region, where the population rate of road crash fatalities consistently increased as per country reports, World Health Organization and Global Burden of Disease estimates. In these countries, the proportion of fatalities involving motorcycles (including powered two or three-wheelers) substantially increased (44%) over the same time (statistically significant). In these countries, the helmet-wearing rate was only 46% for all passengers. These patterns were not observed in LMICs with decreasing population fatality rates. RESULTS: Motorcycle helmet usage rates strongly correlate with decreasing fatalities per 10,000 motorcycles in Low-Income Countries (LICs) and LMICs. Effective interventions (including increasing helmet usage) are urgently needed for motorcycle crash trauma in LMICs, especially where the economy and motorization rapidly grow. National strategies for motorcycle safety, conforming to the Safe System principles, are recommended. CONCLUSIONS: For evidence-based policy formulation, there is a need to continue strengthening data collection, sharing, and use.
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Países em Desenvolvimento , Motocicletas , Humanos , Ásia Meridional , Coleta de Dados , Dispositivos de Proteção da CabeçaRESUMO
BACKGROUND: Road traffic injuries (RTIs) are a leading cause of death and unintentional injuries globally. They claim 1.35 million lives and produce up to 50 million injuries each year, causing a major drain on health systems. Despite this high burden, there is a lack of robust data on the long-term consequences of RTIs, specifically the level of disability experienced by many survivors and its impact on their everyday lives. OBJECTIVE: This study aims to characterize RTIs, disability level, and related consequences affecting adult road traffic crash survivors in 5 low- and middle-income countries (LMICs). In addition, this study estimates the role of demographic and crash- and treatment-related factors in predicting adverse outcomes and disability as well as examining the disability level among patients with RTIs, likelihood of return to normal life, and the environmental factors that may influence these outcomes after discharge from the hospital. METHODS: This prospective observational study was conducted at selected hospitals in Bangladesh, Cambodia, Ethiopia, Mexico, and Zambia. The study sample included all adult patients with RTIs admitted to the hospital for at least 24 hours. Consecutive sampling was performed until the minimum required sample size of 400 was reached for each participating country. Data were collected from patients or their caregivers using a hospital-based surveillance tool administered at the participating sites as well as a telephone-based follow-up instrument administered 1, 3, and 6 months after discharge. Descriptive analysis and multivariate models will be used to estimate the contribution of a range of factors in predicting adverse outcomes, disability, and return to normal life. RESULTS: Enrollment began in June 2021 and was completed in April 2022. Follow-up data collection ended in September 2022. Data analysis is currently underway, with results expected for publication in mid-2023. Expected results include estimates of disability among patients with RTIs as well as identifying the predictors of adverse outcomes, disability, and the likelihood of return to normal life. CONCLUSIONS: Research findings will help better understand the long-term burden of disability from RTIs in the 5 LMICs and the challenges facing survivors of road traffic crashes. They will be used to inform interventions aimed at improving the health care, social, physical, and policy conditions in LMICs that can facilitate recovery and rehabilitation for patients with RTIs, reduce the burden of disability, and enhance their participation in society. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40985.
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We report an 8-year-old girl who developed generalized anhidrosis following presumptive H1N1 infection. Pure autonomic dysfunction is an unusual complication following H1N1 infection and specially generalized anhidrosis without other autonomic dysfunction have not been reported before.
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Hipo-Hidrose/diagnóstico , Hipo-Hidrose/virologia , Vírus da Influenza A Subtipo H1N1/patogenicidade , Influenza Humana/complicações , Criança , Doença Crônica , Feminino , Humanos , Influenza Humana/diagnóstico , Influenza Humana/virologia , Glândulas Sudoríparas/inervação , Glândulas Sudoríparas/fisiopatologia , Glândulas Sudoríparas/virologia , Sistema Nervoso Simpático/fisiopatologia , Sistema Nervoso Simpático/virologiaRESUMO
In developing nations, road traffic crashes involving pedestrians have become a foremost worry. Presently, most of the road safety assessment projects and selection of interventions are still restricted to traditional methods that depend on historical crash data. However, in low and middle-income countries such as India, the availability, reliability, and accuracy of crash data are uncertain. Alternatively, Post Encroachment Time (PET) has added attention as a proximal indicator to examine pedestrian-vehicular potential crashes and address pedestrian risk under mixed traffic conditions. Hence, it will be meaningful to examine if the PET is a good substitute for pedestrian-vehicular crashes and if so, what built environment and pedestrian-level factors influence PET. In this background, the present study establishes a mathematical association between the average PET value of the urban road network level and actual crashes. Afterward, multiple linear regression models are developed to study the impact of the built environment, traffic parameters, and pedestrian-level attributes on PET. The outcomes indicate that vehicle speed, lack of enforcement, absence of traffic signal (for traffic as well as pedestrians), land use type, slum population, inadequate sight distance, pedestrian's state of crossing, and pedestrian's risky crossing behaviour substantially affect the average PET at road network-level.
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Pedestres , Acidentes de Trânsito/prevenção & controle , Ambiente Construído , Humanos , Índia , Reprodutibilidade dos Testes , SegurançaRESUMO
Pedestrian fatalities and injuries are a major public health burden in developing countries. In the safety literature, pedestrian crashes have been modelled predominately using single equation regression models, assuming a single underlying source of risk factors. In contrast, the fatal pedestrian crash counts at a site may be an outcome of multiple sources of risk factors, such as poor road infrastructure, land use type, traffic exposures, and operational parameters, site-specific socio-demographic characteristics, as well as pedestrians' poor risk perception and dangerous crossing behavior, which may be influenced by poor road infrastructure and lack of information, etc. However, these multiple sources are generally overlooked in traditional single equation crash prediction models. In this background, this study postulates, and demonstrates empirically, that the total fatal pedestrian crash counts at the urban road network level may arise from multiple simultaneous and interdependent sources of risk factors, rather than one. Each of these sources may distinctively contribute to the total observed crash count. Intersection-level crash data obtained from the "Kolkata Police", India, is utilized to demonstrate the present modelling methodology. The three-components mixture model and a joint econometric model are developed to predict fatal pedestrian crashes. The study outcomes indicate that the multiple-source risk models perform significantly better than the single equation regression model in terms of prediction ability and goodness-of-fit measures. Moreover, while the single equation model predicts total fatal crash counts for individual sites, the multiple risk source model predicts crash count proportions contributed by each source of risk factors and predicts crashes by a particular source.
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Pedestres , Acidentes de Trânsito , Países em Desenvolvimento , Humanos , Polícia , Fatores de RiscoRESUMO
INTRODUCTION: Tracking progress towards Sustainable Development Goal (SDG) 3·6 of reducing traffic deaths and serious injuries poses a measurement challenge in most low-income and middle-income countries (LMICs) due to large discrepancies between reported official statistics and estimates from global health measurement studies. We assess the extent to which national population censuses and health surveys can fill the information gaps. METHODS: We reviewed questionnaires for nationally representative surveys and censuses conducted since 2000 in LMICs. We identified sources that provide estimates of household ownership of vehicles, incidence of traffic deaths and non-fatal injuries, and prevalence of disability. RESULTS: We identified 802 data sources from 132 LMICs. Sub-Saharan African countries accounted for 43% of all measurements. The number of measurements since 2000 was high, with 97% of the current global LMIC population having at least one measurement for vehicle ownership, 77% for deaths, 90% for non-fatal injuries and 50% for disability due to traffic injuries. Recent data (since 2010) on traffic injuries were available from far fewer countries (deaths: 21 countries; non-fatal injuries: 62 and disability: 12). However, there were many more countries with recent data on less-specific questions about unintentional or all injuries (deaths: 41 countries, non-fatal: 87, disability: 32). CONCLUSION: Traffic injuries are substantially underreported in official statistics of most LMICs. National surveys and censuses provide a viable alternative information source, but despite a large increase in their use to monitor SDGs, traffic injury measurements have not increased. We show that relatively small modifications and additions to questions in forthcoming surveys can provide countries with a way to benchmark their existing surveillance systems and result in a substantial increase in data for tracking road traffic injuries globally.
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Acidentes de Trânsito , Países em Desenvolvimento , Humanos , Incidência , Armazenamento e Recuperação da Informação , PobrezaRESUMO
Urban signalized intersections in Indian cities experience a high share of fatalities and serious injuries. However, assessment of safety using rigorous statistical technique is still in an early stage in India, primarily due to challenges in obtaining crash data, road geometric and traffic data. This study intended to focus on safety issues associated with the planning, design, and operation of signalized intersections, and identify the risk factors affecting the frequency and severity of crashes. Assuming that user behavior is influenced by the design and traffic operational characteristics of a location, this study specifically aims to investigate the effects of geometric, infrastructural, traffic control and land use parameters on the number of crashes and their severity across 52 signalized intersections of Kolkata. These data were associated with crash records from 2011 through 2014, to extract important insights, using statistical tests such as correlation analysis and independent samples t-test, as well as multivariate regression analysis for crash prediction purposes. Based on the assessments carried out in this study, there is evidence that factors such as total approach traffic volume, blocked carriageways, traffic configuration, presence of protected right turning phase, type of minor road, tram stops, etc. significantly affect the frequency of crashes while presence of all-red time, protected right turning phase, blocked carriageway, non-motorized traffic, and visibility of road markings affect the severity of crashes at signalized intersections of Kolkata. In the absence of other road traffic safety studies involving rigorous statistical analysis, these findings can significantly contribute to policy-making decisions that are aimed at improving the safety of signalized intersections in the city of Kolkata.
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In developing countries, pedestrian safety is an issue of major concern. Although an extensive body of literature is available on the identification of elements related to the pedestrian's risk; the studies are primarily conducted in urban areas of developed countries. The findings from these studies may only be partially relevant to the cities of an emerging country such as India. The present study analyzes historical crash records obtained from the "Kolkata Police" and identifies the risk factors at the road network level for the hazardous corridors posing a high risk to the pedestrians. The study findings reveal that pedestrians' fatalities at intersections are associated with a high vehicular volume, higher pedestrian-vehicular interaction, high approach speed, overtaking tendency of vehicles, certain land-use type, encroachment of footpath, inadequate sight distance, inaccessible pedestrian crosswalk, wider minor carriageway, the absence of a pedestrian signal head, and lack of enforcement. On the other hand, the models outcomes reveal that pedestrians' fatalities at midblock road segments are associated with low pedestrian volume, high approach speed, overtaking tendency of the vehicle, encroachment of footpath, on-street parking, wider road width, certain land-use type, inadequate sight distance, insufficient lighting, and inadequate pavement markings.
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Acidentes de Trânsito/mortalidade , Modelos Teóricos , Pedestres , Bases de Dados Factuais , Humanos , Índia/epidemiologia , Distribuição de Poisson , Fatores de Risco , SegurançaRESUMO
Signalized intersections with marked crosswalks enhance pedestrian safety by providing the designated right of way to pedestrians. However, a significant number of pedestrian fatalities occur at signalized intersections, which may primarily be due to pedestrians' violation behaviors. Since pedestrians' fatalities are not uniform across signalized intersections in a city, it may be expected that violations would also vary across the sites. It is thus worthwhile to investigate if the pedestrian signal violation is a good surrogate for fatal pedestrian crashes at signalized intersections, and if so, what behavioral, spatial, and built environment related factors influence such violations. To this end, present study analyzes pedestrian behavior and perceptions across twenty-four signalized intersections in Kolkata city, India, out of which twelve intersections did not record any fatal pedestrian crashes between 2011 and 2016 and the remaining twelve experienced at least three or more fatal pedestrian crashes over the same period. Using data from the video-graphic survey at these twenty-four signalized intersections violation behaviors are extracted along with personal attributes at the pedestrian level. Further, pedestrian perception surveys are carried out at each of the twenty-four sites, to obtain a user's perception of safety and satisfaction. Results indicate that pedestrians' signal violations behavior and dissatisfaction are statistically significantly higher at locations with recorded fatal pedestrian crashes. Results from different models and analysis clearly pointed out several planning and design deficiencies such as longer waiting time before crossing, higher pedestrian-vehicular interaction, pedestrian's state of crossing, and a number of personal level attributes such as pedestrian's intended mode of transportation and their state of journey, pedestrian's home location, pedestrian's socio-demographic characteristics as important predictors of pedestrians' violation behavior. The methodology and findings are useful not only for proactive safety improvement at signalized intersections but also to proactively identify potential unsafe sites.
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Acidentes de Trânsito/mortalidade , Pedestres/psicologia , Adolescente , Adulto , Idoso , Ambiente Construído , Feminino , Humanos , Índia , Masculino , Pessoa de Meia-Idade , Pedestres/estatística & dados numéricos , Adulto JovemRESUMO
Urban intersections in India constitute a significant share of pedestrian fatalities. However, model-based prediction of pedestrian fatalities is still in a nascent stage in India. This study proposes an artificial neural network (ANN) technique to develop a pedestrian fatal crash frequency model at the intersection level. In this study, three activation functions are used along with four different learning algorithms to build different combinations of ANN models. In each of these combinations, the number of neurons in the hidden layer is varied by trial and error method, and the best results are considered. In this way, 12 sets of pedestrian fatal crash predictive models are developed. Out of these, Bayesian Regularization Neural Network consisting of 13 neurons in the hidden layer with 'hyperbolic tangent-sigmoid' activation function is found to be the best-fit model. Finally, based on sensitivity analysis, it is found that the 'approaching speed' of the motorized vehicle has the most significant influence on the fatal pedestrian crashes. 'Logarithm of average daily traffic' (ADT) volume is found to be the second most sensitive variable. Pedestrian-vehicular interaction concerning 'pedestrian-vehicular volume ratio' and lack of 'accessibility of pedestrian cross-walk' are found to be approximately as sensible as 'logarithm of ADT'.
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Acidentes de Trânsito/estatística & dados numéricos , Países em Desenvolvimento/estatística & dados numéricos , Redes Neurais de Computação , Pedestres/estatística & dados numéricos , Acidentes de Trânsito/mortalidade , Teorema de Bayes , Planejamento Ambiental , Humanos , Fatores de Risco , Segurança/estatística & dados numéricosRESUMO
Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts--variation over and above that accounted for by the Poisson density. The extra--variation--or dispersion--is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models--tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31-40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions. This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with non-informative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites.
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Acidentes de Trânsito/estatística & dados numéricos , Condução de Veículo/estatística & dados numéricos , Automóveis/estatística & dados numéricos , Modelos Estatísticos , Teorema de Bayes , Georgia/epidemiologia , Humanos , Distribuição de Poisson , Fatores de RiscoRESUMO
Advances in safety research--trying to improve the collective understanding of motor vehicle crash causes and contributing factors--rest upon the pursuit of numerous lines of research inquiry. The research community has focused considerable attention on analytical methods development (negative binomial models, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might logically seek to know which lines of inquiry might provide the most significant improvements in understanding crash causation and/or prediction. It is the contention of this paper that the exclusion of important variables (causal or surrogate measures of causal variables) cause omitted variable bias in model estimation and is an important and neglected line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models--but offer significant opportunities to better understand contributing factors and/or causes of crashes. This study examines the role of important variables (other than Average Annual Daily Traffic (AADT)) that are generally omitted from intersection crash prediction models. In addition to the geometric and traffic regulatory information of intersection, the proposed model includes many spatial factors such as local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools--representing a mix of potential environmental and human factors that are theoretically important, but rarely used. Results suggest that these variables in addition to AADT have significant explanatory power, and their exclusion leads to omitted variable bias. Provided is evidence that variable exclusion overstates the effect of minor road AADT by as much as 40% and major road AADT by 14%.