Imported Dengue cases, weather variation and autochthonous Dengue incidence in Cairns, Australia

BACKGROUND Dengue fever (DF) outbreaks often arise from imported DF cases in Cairns, Australia. Few studies have incorporated imported DF cases in the estimation of the relationship between weather variability and incidence of autochthonous DF. The study aimed to examine the impact of weather variability on autochthonous DF infection after accounting for imported DF cases and then to explore the possibility of developing an empirical forecast system. METHODOLOGY/PRINCIPAL FINDS Data on weather variables, notified DF cases (including those acquired locally and overseas), and population size in Cairns were supplied by the Australian Bureau of Meteorology, Queensland Health, and Australian Bureau of Statistics. A time-series negative-binomial hurdle model was used to assess the effects of imported DF cases and weather variability on autochthonous DF incidence. Our results showed that monthly autochthonous DF incidences were significantly associated with monthly imported DF cases (Relative Risk (RR):1.52; 95% confidence interval (CI): 1.01-2.28), monthly minimum temperature ((o)C) (RR: 2.28; 95% CI: 1.77-2.93), monthly relative humidity (%) (RR: 1.21; 95% CI: 1.06-1.37), monthly rainfall (mm) (RR: 0.50; 95% CI: 0.31-0.81) and monthly standard deviation of daily relative humidity (%) (RR: 1.27; 95% CI: 1.08-1.50). In the zero hurdle component, the occurrence of monthly autochthonous DF cases was significantly associated with monthly minimum temperature (Odds Ratio (OR): 1.64; 95% CI: 1.01-2.67). CONCLUSIONS/SIGNIFICANCE Our research suggested that incidences of monthly autochthonous DF were strongly positively associated with monthly imported DF cases, local minimum temperature and inter-month relative humidity variability in Cairns. Moreover, DF outbreak in Cairns was driven by imported DF cases only under favourable seasons and weather conditions in the study.

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots

Hot spot identification (HSID) aims to identify potential sites—roadway segments, intersections, crosswalks, interchanges, ramps, etc.—with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing with preponderance of zeros problem or right skewed dataset.

GPU accelerated MCMC for modelling terrorist activity

The use of graphical processing unit (GPU) parallel processing is becoming a part of mainstream statistical practice. The reliance of Bayesian statistics on Markov Chain Monte Carlo (MCMC) methods makes the applicability of parallel processing not immediately obvious. It is illustrated that there are substantial gains in improved computational time for MCMC and other methods of evaluation by computing the likelihood using GPU parallel processing. Examples use data from the Global Terrorism Database to model terrorist activity in Colombia from 2000 through 2010 and a likelihood based on the explicit convolution of two negative-binomial processes. Results show decreases in computational time by a factor of over 200. Factors influencing these improvements and guidelines for programming parallel implementations of the likelihood are discussed.

Outbreak detection algorithms for seasonal disease data : a case study using Ross River virus disease

Background Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. Methods We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. Results The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. Conclusions This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

Awards before and after the Nobel Prize : a Matthew effect and/or a ticket to one's own funeral?

The primary aim of this descriptive exploration of scientists’ life cycle award patterns is to evaluate whether awards breed further awards and identify researcher experiences after reception of the Nobel Prize. To achieve this goal, we collected data on the number of awards received each year for 50 years before and after Nobel Prize reception by all 1901–2000 Nobel laureates in physics, chemistry, and medicine or physiology. Our results indicate an increasing rate of awards before Nobel reception, reaching the summit precisely in the year of the Nobel Prize. After this pinnacle year, awards drop sharply. This result is confirmed by separate analyses of three different disciplines and by a random-effects negative binomial regression model. Such an effect, however, does not emerge for more recent Nobel laureates (1971–2000). In addition, Nobelists in medicine or physiology generate more awards shortly before and after prize reception, whereas laureates in chemistry attract more awards as time progresses.

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.

Investigation of pedestrian crashes on two-way two-lane rural roads in Ethiopia

Understanding pedestrian crash causes and contributing factors in developing countries is critically important as they account for about 55% of all traffic crashes. Not surprisingly, considerable attention in the literature has been paid to road traffic crash prediction models and methodologies in developing countries of late. Despite this interest, there are significant challenges confronting safety managers in developing countries. For example, in spite of the prominence of pedestrian crashes occurring on two-way two-lane rural roads, it has proven difficult to develop pedestrian crash prediction models due to a lack of both traffic and pedestrian exposure data. This general lack of available data has further hampered identification of pedestrian crash causes and subsequent estimation of pedestrian safety performance functions. The challenges are similar across developing nations, where little is known about the relationship between pedestrian crashes, traffic flow, and road environment variables on rural two-way roads, and where unique predictor variables may be needed to capture the unique crash risk circumstances. This paper describes pedestrian crash safety performance functions for two-way two-lane rural roads in Ethiopia as a function of traffic flow, pedestrian flows, and road geometry characteristics. In particular, random parameter negative binomial model was used to investigate pedestrian crashes. The models and their interpretations make important contributions to road crash analysis and prevention in developing countries. They also assist in the identification of the contributing factors to pedestrian crashes, with the intent to identify potential design and operational improvements.

Maternal pregravid body mass index and child hospital admissions in the first 5 years of life: results from an Australian birth cohort

Objectives: To examine the association of maternal pregravid body mass index (BMI) and child offspring, all-cause hospitalisations in the first 5 years of life. Methods: Prospective birth cohort study. From 2006 to 2011, 2779 pregnant women (2807 children) were enrolled in the Environments for Healthy Living: Griffith birth cohort study in South-East Queensland, Australia. Hospital delivery record and self-report baseline survey of maternal, household and demographic factors during pregnancy were linked to the Queensland Hospital Admitted Patients Data Collection from 1 November 2006 to 30 June 2012, for child admissions. Maternal pregravid BMI was classified as underweight (<18.5 kg m−2), normal weight (18.5–24.9 kg m−2), overweight (25.0–29.9 kg m−2) or obese (30 kg m−2). Main outcomes were the total number of child hospital admissions and ICD-10-AM diagnostic groupings in the first 5 years of life. Negative binomial regression models were calculated, adjusting for follow-up duration, demographic and health factors. The cohort comprised 8397.9 person years (PYs) follow-up. Results: Children of mothers who were classified as obese had an increased risk of all-cause hospital admissions in the first 5 years of life than the children of mothers with a normal BMI (adjusted rate ratio (RR) =1.48, 95% confidence interval 1.10–1.98). Conditions of the nervous system, infections, metabolic conditions, perinatal conditions, injuries and respiratory conditions were excessive, in both absolute and relative terms, for children of obese mothers, with RRs ranging from 1.3–4.0 (PYs adjusted). Children of mothers who were underweight were 1.8 times more likely to sustain an injury or poisoning than children of normal-weight mothers (PYs adjusted). Conclusion: Results suggest that if the intergenerational impact of maternal obesity (and similarly issues related to underweight) could be addressed, a significant reduction in child health care use, costs and public health burden would be likely.

Do motor vehicle crashes arise from single or multiple unique risk processes? An inquiry into crash causes and modelling

Crashes at any particular transport network location consist of a chain of events arising from a multitude of potential causes and/or contributing factors whose nature is likely to reflect geometric characteristics of the road, spatial effects of the surrounding environment, and human behavioural factors. It is postulated that these potential contributing factors do not arise from the same underlying risk process, and thus should be explicitly modelled and understood. The state of the practice in road safety network management applies a safety performance function that represents a single risk process to explain crash variability across network sites. This study aims to elucidate the importance of differentiating among various underlying risk processes contributing to the observed crash count at any particular network location. To demonstrate the principle of this theoretical and corresponding methodological approach, the study explores engineering (e.g. segment length, speed limit) and unobserved spatial factors (e.g. climatic factors, presence of schools) as two explicit sources of crash contributing factors. A Bayesian Latent Class (BLC) analysis is used to explore these two sources and to incorporate prior information about their contribution to crash occurrence. The methodology is applied to the state controlled roads in Queensland, Australia and the results are compared with the traditional Negative Binomial (NB) model. A comparison of goodness of fit measures indicates that the model with a double risk process outperforms the single risk process NB model, and thus indicating the need for further research to capture all the three crash generation processes into the SPFs.

Using simulation techniques to investigate methods to determine resistance of helminths to anthelmintic treatment

The widespread and increasing resistance of internal parasites to anthelmintic control is a serious problem for the Australian sheep and wool industry. As part of control programmes, laboratories use the Faecal Egg Count Reduction Test (FECRT) to determine resistance to anthelmintics. It is important to have confidence in the measure of resistance, not only for the producer planning a drenching programme but also for companies investigating the efficacy of their products. The determination of resistance and corresponding confidence limits as given in anthelmintic efficacy guidelines of the Standing Committee on Agriculture (SCA) is based on a number of assumptions. This study evaluated the appropriateness of these assumptions for typical data and compared the effectiveness of the standard FECRT procedure with the effectiveness of alternative procedures. Several sets of historical experimental data from sheep and goats were analysed to determine that a negative binomial distribution was a more appropriate distribution to describe pre-treatment helminth egg counts in faeces than a normal distribution. Simulated egg counts for control animals were generated stochastically from negative binomial distributions and those for treated animals from negative binomial and binomial distributions. Three methods for determining resistance when percent reduction is based on arithmetic means were applied. The first was that advocated in the SCA guidelines, the second similar to the first but basing the variance estimates on negative binomial distributions, and the third using Wadley’s method with the distribution of the response variate assumed negative binomial and a logit link transformation. These were also compared with a fourth method recommended by the International Co-operation on Harmonisation of Technical Requirements for Registration of Veterinary Medicinal Products (VICH) programme, in which percent reduction is based on the geometric means. A wide selection of parameters was investigated and for each set 1000 simulations run. Percent reduction and confidence limits were then calculated for the methods, together with the number of times in each set of 1000 simulations the theoretical percent reduction fell within the estimated confidence limits and the number of times resistance would have been said to occur. These simulations provide the basis for setting conditions under which the methods could be recommended. The authors show that given the distribution of helminth egg counts found in Queensland flocks, the method based on arithmetic not geometric means should be used and suggest that resistance be redefined as occurring when the upper level of percent reduction is less than 95%. At least ten animals per group are required in most circumstances, though even 20 may be insufficient where effectiveness of the product is close to the cut off point for defining resistance.

A Bayesian latent class safety performance function for identifying motor vehicle crash blackspots

The current state of the practice in Blackspot Identification (BSI) utilizes safety performance functions based on total crash counts to identify transport system sites with potentially high crash risk. This paper postulates that total crash count variation over a transport network is a result of multiple distinct crash generating processes including geometric characteristics of the road, spatial features of the surrounding environment, and driver behaviour factors. However, these multiple sources are ignored in current modelling methodologies in both trying to explain or predict crash frequencies across sites. Instead, current practice employs models that imply that a single underlying crash generating process exists. The model mis-specification may lead to correlating crashes with the incorrect sources of contributing factors (e.g. concluding a crash is predominately caused by a geometric feature when it is a behavioural issue), which may ultimately lead to inefficient use of public funds and misidentification of true blackspots. This study aims to propose a latent class model consistent with a multiple crash process theory, and to investigate the influence this model has on correctly identifying crash blackspots. We first present the theoretical and corresponding methodological approach in which a Bayesian Latent Class (BLC) model is estimated assuming that crashes arise from two distinct risk generating processes including engineering and unobserved spatial factors. The Bayesian model is used to incorporate prior information about the contribution of each underlying process to the total crash count. The methodology is applied to the state-controlled roads in Queensland, Australia and the results are compared to an Empirical Bayesian Negative Binomial (EB-NB) model. A comparison of goodness of fit measures illustrates significantly improved performance of the proposed model compared to the NB model. The detection of blackspots was also improved when compared to the EB-NB model. In addition, modelling crashes as the result of two fundamentally separate underlying processes reveals more detailed information about unobserved crash causes.

Kesämökkikäynnin virkistysarvon määrittäminen matkakustannusmenetelmällä

Tämän tutkielman tarkoituksena on määrittää kesämökkikäynnin virkistysarvo. Aihetta ei ole aikaisemmin tutkittu, vaikka kesämökkeily on merkittävä osa suomalaista elämää. Kesämökkikäynnin virkistysarvo tarkoittaa hyötyä, jonka yksilö saa kesämökillä virkistäytymisestä. Virkistäytyminen kesämökillä pitää sisällään kaiken kesämökillä ja sen ympäristössä tapahtuvan harrastamisen ja rentoutumisen. Koska ympäristö on tärkeässä osassa mökillä virkistäytymisessä, tässä tutkielmassa on lisäksi tarkoitus tutkia, kuinka mökkiympäristön ominaisuudet vaikuttavat virkistysarvoon. Tarkasteltavina ympäristön ominaisuuksina ovat virkistäytymisen estävät leväkukinnot ja mökin rannattomuus. Koska mökkeily toisaalta myös kuormittaa ympäristöä, tutkielmassa tutkitaan myös, kuinka sähköistys, ympäristöä kuormittava kesämökin ominaisuus, vaikuttaa virkistysarvoon. Virkistysarvo on markkinaton hyöty, joten sen määrittämiseen on käytettävä jotain markkinattomien hyödykkeiden arvottamismenetelmää. Tässä työssä arvottaminen tapahtuu matkakustannusmenetelmällä, jota käytetään yleisesti ympäristön tarjoamien virkistyspalveluiden taloudelliseen arvottamiseen. Kesämökkikäyntien kysyntää kuvaava matkakustannusmallin ekonometrinen mallintaminen suoritetaan negatiivisella binomimallilla. Tutkielman tulosten mukaan noin neljän päivän pituinen käynti sähköistetyllä kesämökillä, jossa on ranta eivätkä levät häiritse virkistäytymistä, tuottaa 167-205 euron suuruisen virkistyshyödyn. Virkistäytymisen estävät leväkukinnot laskevat arvoa 40 prosentilla ja mökin rannattomuus 45 prosentilla. Käynti sähköistetyllä mökillä tuottaa 3-5 prosenttia korkeamman virkistyshyödyn kuin käynti sähköistämättömällä mökillä. Suomessa kesän aikana tehtävien mökkikäyntien yhteenlaskettu virkistyshyöty on 430-530 miljoonaa, jos mökillä on ranta, jossa levistä ei ole haittaa. Häiritsevät leväkukinnot laskevat yhteenlaskettua virkistyshyötyä 30 miljoonalla ja rannattomuus 10-20 miljoonalla. Sähköistys nostaa yhteenlaskettua virkistyshyötyä 20-30 miljoonalla eurolla.

Catch rates and demographics of loggerhead sea turtles (Caretta caretta) captured from the Charleston, South Carolina, shipping channel during the period of mandatory use of turtle excluder devices (TEDs)

Trawling was conducted in the Charleston, South Carolina, shipping channel between May and August during 2004–07 to evaluate loggerhead sea turtle (Caretta caretta) catch rates and demographic distributions. Two hundred and twenty individual loggerheads were captured in 432 trawling events during eight sampling periods lasting 2–10 days each. Catch was analyzed by using a generalized linear model. Data were fitted to a negative binomial distribution with the log of standardized sampling effort (i.e., an hour of sampling with a net head rope length standardized to 30.5 m) for each event treated as an offset term. Among 21 variables, factors, and interactions, five terms were significant in the final model, which accounted for 45% of model deviance. Highly significant differences in catch were noted among sampling periods and sampling locations within the channel, with greatest catch furthest seaward consistent with historical observations. Loggerhead sea turtle catch rates in 2004–07 were greater than in 1991–92 when mandatory use of turtle excluder devices was beginning to be phased in. Concurrent with increased catch rates, loggerheads captured in 2004–07 were larger than in 1991–92. Eighty-five percent of loggerheads captured were ≤75.0 cm straight-line carapace length (nuchal notch to tip of carapace) and there was a 3.9:1 female-to-male bias, consistent with limited data for this location two decades earlier. Only juvenile loggerheads ≤75.0 cm possessed haplotypes other than CC-A01 or CC-A02 that dominate in the region. Six rare and one un-described haplotype were predominantly found in June 2004.

Evaluation of a Pound Net Leader Designed to Reduce Sea Turtle Bycatch

Offshore pound net leaders in the southern portion of Chesapeake Bay in Virginia waters were documented to incidentally take protected loggerhead, Caretta caretta, and Kemp’s ridley, Lepidochelys kempii, sea turtles. Because of these losses, NOAA’s National Marine Fisheries Service (NMFS) in 2004 closed the area to offshore pound net leaders annually from 6 May to 15 July and initiated a study of an experimental leader design that replaced the top two-thirds of the traditional mesh panel leader with vertical ropes (0.95 cm) spaced 61 cm apart. This experimental leader was tested on four pound net sites on the eastern shore of Chesapeake Bay in 2004 and 2005. During the 2 trial periods, 21 loggerhead and Kemp’s ridley sea turtles were found interacting with the control leader and 1 leatherback turtle, Dermochelys coriacea, was found interacting with the experimental leader. Results of a negative binomial regression analysis comparing the two leader designs found the experimental leader significantly reduced sea turtle interactions (p=0.03). Finfish were sampled from the pound nets in the study to assess finfish catch performance differences between the two leader designs. Although the conclusions from this element of the experiment are not robust, paired t-test and Wilcoxon signed rank test results determined no significant harvest weight difference between the two leaders. Kolmogorov-Smirnov tests did not reveal any substantive size selectivity differences between the two leaders.

Impacto das etapas do tratamento orto-cirúrgico na qualidade de vida de pacientes portadores de deformidades dentofaciais

Pacientes portadores de deformidades dentofaciais podem relatar dificuldades de mastigação e fala, desordens temporomandibulares, preocupação com a imagem corporal e baixa autoestima. Frequentemente, buscam tratamento orto-cirúrgico pela motivação de obter melhora notável nos aspectos estético, funcional e psicossocial. A evidência atualmente disponível sobre os benefícios na qualidade de vida relacionada à saúde bucal desta modalidade terapêutica ainda não é conclusiva, devido à diversidade de metodologias adotadas entre os estudos existentes, majoritariamente realizados na América do Norte, Europa, Oriente Médio e Ásia. Logo, é essencial utilizar instrumentos específicos para avaliar os efeitos desta modalidade de tratamento também na vida diária dos pacientes brasileiros. O propósito do presente estudo transversal foi determinar o impacto que o tratamento orto-cirúrgico exerce sobre a percepção de qualidade de vida dos pacientes portadores de deformidades dentofaciais, bem como a influência exercida pelo gênero, idade, renda, escolaridade e características da má oclusão, nas quatro etapas inerentes a esta modalidade de tratamento: (1) Inicial; (2) Preparo ortodôntico para a cirurgia; (3) Pós-cirúrgico; e (4) Contenção (pós-tratamento). Duzentos e cinquenta e quatro pacientes foram entrevistados em três importantes centros de atendimento na cidade do Rio de Janeiro. A qualidade de vida foi avaliada pelos questionários OHIP-14 (Oral Health Impact Profile - Short Version) e pelo OQLQ (Orthognathic Quality of Life Questionnaire) em suas versões traduzidas e validadas para o português. A gravidade da má oclusão e autopercepção estética foram avaliadas com base no Índice de Necessidade de Tratamento Ortodôntico (IOTN) e pelo Índice de Estética Dental (DAI). A análise dos dados foi efetuada pelos testes qui-quadrado, Kruskal-Wallis e modelos de regressão binomial negativa múltipla. Os pacientes dos quatro grupos foram semelhantes em relação ao gênero (p = 0,463), escolaridade (p = 0,276) e renda familiar (p = 0,100). Entre os entrevistados houve o predomínio de mulheres, com ensino médio completo e renda familiar entre 2 e 3 salários mínimos, portadores de má oclusão de Classe III de Angle grave. No modelo de regressão binomial negativa ajustado para os fatores gênero, idade, renda familiar e escolaridade, a qualidade de vida aferida pelo OHIP-14 demonstrou que o grupo Inicial sofreu impactos mais negativos do que os grupos Pós-cirúrgico, Preparo e Contenção; o OQLQ indicou que o grupo Inicial sofreu impactos mais negativos do que os grupos Preparo, Pós-cirúrgico e Contenção, nesta sequência. Não foi detectada influência da idade, renda e escolaridade nestes resultados. Foi observado que o gênero feminino sofreu mais impacto negativo na qualidade de vida, principalmente nas dimensões relativas à função e a aspectos sociais. Concluiu-se que os pacientes que finalizaram o tratamento orto-cirúrgico apresentaram como benefícios menores impactos na qualidade de vida específica e relacionada à saúde bucal, melhor autopercepção estética e menor gravidade da má oclusão, em comparação aos pacientes nas etapas pré e pós-cirúrgica e aos pacientes portadores de deformidades dentofaciais em busca de tratamento.