L’internationalisation des jeunes entreprises de hautes technologies : Le rôle des compétences internationales des fondateurs [The internationalization of new technology based firms : the role of the international skills of founding teams]

Extending recent research on the importance of specific resources and skills for the internationalization of start-ups, this article tests a negative binomial model on a sample of 520 recently created high technology firms from the UK and Germany. The results show that previous international experience of entrepreneurs facilitates the rapid penetration of foreign markets, especially when the company features a clear and deliberate strategic intent of internationalization from the outset. This research provides one of the first empirical studies linking the influence of entrepreneurial teams to a high probability of success in the internationalization of high-technology ventures.

Factors associated with common mental disorders during the transition to adulthood : do emerging adults and ‘early starters’ differ? Findings from an Australian birth cohort study

Background: Despite increasing diversity in pathways to adulthood, choices available to young people are influenced by environmental, familial and individual factors, namely access to socioeconomic resources, family support and mental and physical health status. Young people from families with higher socioeconomic position (SEP) are more likely to pursue tertiary education and delay entry to adulthood, whereas those from low socioeconomic backgrounds are less likely to attain higher education or training, and more likely to partner and become parents early. The first group are commonly termed ‘emerging adults’ and the latter group ‘early starters’. Mental health disorders during this transition can seriously disrupt psychological, social and academic development as well as employment prospects. Depression, anxiety and most substance use disorders have early onset during adolescence and early adulthood with approximately three quarters of lifetime psychiatric disorders having emerged by 24 years of age. Aims: This thesis aimed to explore the relationships between mental health, sociodemographic factors and family functioning during the transition to adulthood. Four areas were investigated: 1) The key differences between emerging adults and ‘early starters’, were examined and focused on a series of social, economic, and demographic factors as well as DSM-IV diagnoses; 2) Methodological issues associated with the measurement of depression and anxiety in young adults were explored by comparing a quantitative measure of symptoms of anxiety and depression (Achenbach’s YSR and YASR internalising scales) with DSM-IV diagnosed depression and anxiety. 3) The association between family SEP and DSM-IV depression and anxiety was examined in relation to the different pathways to adulthood. 4) Finally, the association between pregnancy loss, abortion and miscarriage, and DSM-IV diagnoses of common psychiatric disorders was assessed in young women who reported early parenting, experiencing a pregnancy loss, or who had never been pregnant. Methods: Data were taken from the Mater University Study of Pregnancy (MUSP), a large birth cohort started in 1981 in Brisbane, Australia. 7223 mothers and their children were assessed five times, at 6 months, 5, 14 and 21 years after birth. Over 3700 young adults, aged 18 to 23 years, were interviewed at the 21-year phase. Respondents completed an extensive series of self-reported questionnaires and a computerised structured psychiatric interview. Three outcomes were assessed at the 21-year phase. Mental health disorders diagnosed by a computerised structured psychiatric interview (CIDI-Auto), the prevalence of DSM-IV depression, anxiety and substance use disorders within the previous 12-month, during the transition (between ages of 18 and 23 years) or lifetime were examined. The primary outcome “current stage in the transition to adulthood” was developed using a measure conceptually constructed from the literature. The measure was based on important demographic markers, and these defined four independent groups: emerging adults (single with no children and living with parents), and three categories of ‘early starter’, singles (with no children or partner, living independently), those with a partner (married or cohabitating but without children) and parents. Early pregnancy loss was assessed using a measure that also defined four independent groups and was based on pregnancy outcomes in the young women This categorised the young women into those who were never pregnant, women who gave birth to a live child, and women who reported some form of pregnancy loss, either an abortion or a spontaneous miscarriage. A series of analyses were undertaken to test the study aims. Potential confounding and mediating factors were prospectively measured between the child’s birth and the 21-year phase. Binomial and multinomial logistic regression was used to estimate the risk of relevant outcomes, and the associations were reported as odds ratios (OR) and 95% confidence intervals (95%CI). Key findings: The thesis makes a number of important contributions to our understanding of the transition to adulthood, particularly in relation to the mental health consequences associated with different pathways. Firstly, findings from the thesis clearly showed that young people who parented or partnered early fared worse across most of the economic and social factors as well as the common mental disorders when compared to emerging adults. That is, young people who became early parents were also more likely to experience recent anxiety (OR=2.0, 95%CI 1.5-2.8) and depression (OR=1.7, 95%CI 1.1-2.7) than were emerging adults after taking into account a range of confounding factors. Singles and those partnering early also had higher rates of lifetime anxiety and depression than emerging adults. Young people who partnered early, but were without children, had decreased odds of recent depression; this may be due to the protective effect of early marriage against depression. It was also found that young people who form families early had an increased risk of cigarette smoking (parents OR=3.7, 95%CI 2.9-4.8) compared to emerging adults, but not heavy alcohol (parents OR=0.4, 95%CI 0.3-0.6) or recent illicit drug use. The high rates of cigarette smoking and tobacco use disorders in ‘early starters’ were explained by common risk factors related to early adversity and lower SEP. Having a child and early marriage may well function as a ‘turning point’ for some young people, it is not clear whether this is due to a conscious decision to disengage from a previous ‘substance using’ lifestyle or simply that they no longer have the time to devote to such activities because of child caring. In relation to the methodological issues associated with assessing common mental disorders in young adults, it was found that although the Achenbach empirical internalising scales successfully predicted both later DSM-IV depression (YSR OR=2.3, 95%CI 1.7-3.1) and concurrently diagnosed depression (YASR OR=6.9, 95%CI 5.0- 9.5) and anxiety (YASR OR=5.1, 95%CI 3.8- 6.7), the scales discriminated poorly between young people with or without DSM-IV diagnosed mood disorder. Sensitivity values (the proportion of true positives) for the internalising scales were surprisingly low. Only a third of young people with current DSM-IV depression (range for each of the scales was between 34% to 42%) were correctly identified as cases by the YASR internalising scales, and only a quarter with current anxiety disorder (range of 23% to 31%) were correctly identified. Also, use of the DSM-oriented scales increased sensitivity only marginally (for depression between 2-8%, and anxiety between 2-6%) above the standard Achenbach scales. This is despite the fact that the DSM-oriented scales were originally developed to overcome the poor prediction of DSM-IV diagnoses by the Achenbach scales. The internalising scales, both standard and DSM-oriented, were much more effective at identifying young people with comorbid depression and anxiety, with OR’s 10.1 to 21.7 depending on the internalising scale used. SEP is an important predictor of both an early transition to adulthood and the experience of anxiety during that time Family income during adolescence was a strong predictor of early parenting and partnering before age 24 but not early independent living. Compared to families in the upper quintile, young people from families with low income were nearly twice as likely to live with a partner and four times more likely to become parents (OR ranged from 2.6 to 4.0). This association remained after adjusting for current employment and education level. Children raised in low income families were 30% more likely to have an anxiety disorder (OR=1.3, 95%CI 0.9-1.9), but not depression, as young adults when compared to children from wealthier families. Emerging adults and ‘early starters’ from low income families did not differ in their likelihood of having a later anxiety disorder. Young women reporting a pregnancy loss had nearly three times the odds of experiencing a lifetime illicit drug disorder (excluding cannabis) [abortion OR=3.6, 95%CI 2.0-6.7 and miscarriage OR=2.6, 95%CI 1.2-5.4]. Abortion was associated with alcohol use disorder (OR=2.1, 95%CI 1.3- 3.5) and 12-month depression (OR=1.9, 95%CI 1.1- 3.1). These finding suggest that the association identified by Fergusson et al between abortion and later psychiatric disorders in young women may be due to pregnancy loss and not to abortion, per se. Conclusion: Findings from this thesis support the view that young people who parent or partner early have a greater burden of depression and anxiety when compared to emerging adults. As well, young women experiencing pregnancy loss, from either abortion or miscarriage, are more likely to experience depression and anxiety than are those who give birth to a live infant or who have never been pregnant. Depression, anxiety and substance use disorders often go unrecognised and untreated in young people; this is especially true in young people from lower SEP. Early identification of these common mental health disorders is important, as depression and anxiety experienced during the transition to adulthood have been found to seriously disrupt an individual’s social, educational and economic prospects in later life.

Sampling stored-product insect pests : a comparison of four statistical sampling models for probability of pest detection

Background: Developing sampling strategies to target biological pests such as insects in stored grain is inherently difficult owing to species biology and behavioural characteristics. The design of robust sampling programmes should be based on an underlying statistical distribution that is sufficiently flexible to capture variations in the spatial distribution of the target species. Results: Comparisons are made of the accuracy of four probability-of-detection sampling models - the negative binomial model,1 the Poisson model,1 the double logarithmic model2 and the compound model3 - for detection of insects over a broad range of insect densities. Although the double log and negative binomial models performed well under specific conditions, it is shown that, of the four models examined, the compound model performed the best over a broad range of insect spatial distributions and densities. In particular, this model predicted well the number of samples required when insect density was high and clumped within experimental storages. Conclusions: This paper reinforces the need for effective sampling programs designed to detect insects over a broad range of spatial distributions. The compound model is robust over a broad range of insect densities and leads to substantial improvement in detection probabilities within highly variable systems such as grain storage.

Incidence of unilateral arm lymphoedema after breast cancer : a systematic review and meta-analysis

Background The body of evidence related to breast-cancer-related lymphoedema incidence and risk factors has substantially grown and improved in quality over the past decade. We assessed the incidence of unilateral arm lymphoedema after breast cancer and explored the evidence available for lymphoedema risk factors. Methods We searched Academic Search Elite, Cumulative Index to Nursing and Allied Health, Cochrane Central Register of Controlled Trials (clinical trials), and Medline for research articles that assessed the incidence or prevalence of, or risk factors for, arm lymphoedema after breast cancer, published between January 1, 2000, and June 30, 2012. We extracted incidence data and calculated corresponding exact binomial 95% CIs. We used random effects models to calculate a pooled overall estimate of lymphoedema incidence, with subgroup analyses to assess the effect of different study designs, countries of study origin, diagnostic methods, time since diagnosis, and extent of axillary surgery. We assessed risk factors and collated them into four levels of evidence, depending on consistency of findings and quality and quantity of studies contributing to findings. Findings 72 studies met the inclusion criteria for the assessment of lymphoedema incidence, giving a pooled estimate of 16·6% (95% CI 13·6–20·2). Our estimate was 21·4% (14·9–29·8) when restricted to data from prospective cohort studies (30 studies). The incidence of arm lymphoedema seemed to increase up to 2 years after diagnosis or surgery of breast cancer (24 studies with time since diagnosis or surgery of 12 to <24 months; 18·9%, 14·2–24·7), was highest when assessed by more than one diagnostic method (nine studies; 28·2%, 11·8–53·5), and was about four times higher in women who had an axillary-lymph-node dissection (18 studies; 19·9%, 13·5–28·2) than it was in those who had sentinel-node biopsy (18 studies; 5·6%, 6·1–7·9). 29 studies met the inclusion criteria for the assessment of risk factors. Risk factors that had a strong level of evidence were extensive surgery (ie, axillary-lymph-node dissection, greater number of lymph nodes dissected, mastectomy) and being overweight or obese. Interpretation Our findings suggest that more than one in five women who survive breast cancer will develop arm lymphoedema. A clear need exists for improved understanding of contributing risk factors, as well as of prevention and management strategies to reduce the individual and public health burden of this disabling and distressing disorder.

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.

Approximate Bayesian computation using auxiliary model based estimates

We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms using indirect infer- ence. We embed this approach within a sequential Monte Carlo algorithm that is completely adaptive. This methodological development was motivated by an application involving data on macroparasite population evolution modelled with a trivariate Markov process. The main objective of the analysis is to compare inferences on the Markov process when considering two di®erent indirect mod- els. The two indirect models are based on a Beta-Binomial model and a three component mixture of Binomials, with the former providing a better ¯t to the observed data.

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.

Geographical distribution of human Schistosoma japonicum infection in the Philippines : tools to support disease control and further elimination

S. japonicum infection is believed to be endemic in 28 of the 80 provinces of the Philippines and the most recent data on schistosomiasis prevalence have shown considerable variability between provinces. In order to increase the efficient allocation of parasitic disease control resources in the country, we aimed to describe the small scale spatial variation in S. japonicum prevalence across the Philippines, quantify the role of the physical environment in driving the spatial variation of S. japonicum, and develop a predictive risk map of S. japonicum infection. Data on S. japonicum infection from 35,754 individuals across the country were geo-located at the barangay level and included in the analysis. The analysis was then stratified geographically for Luzon, the Visayas and Mindanao. Zero-inflated binomial Bayesian geostatistical models of S. japonicum prevalence were developed and diagnostic uncertainty was incorporated. Results of the analysis show that in the three regions, males and individuals aged ≥ 20 years had significantly higher prevalence of S. japonicum compared with females and children <5 years. The role of the environmental variables differed between regions of the Philippines. S. japonicum infection was widespread in the Visayas whereas it was much more focal in Luzon and Mindanao. This analysis revealed significant spatial variation in prevalence of S. japonicum infection in the Philippines. This suggests that a spatially targeted approach to schistosomiasis interventions, including mass drug administration, is warranted. When financially possible, additional schistosomiasis surveys should be prioritized to areas identified to be at high risk, but which were underrepresented in our dataset.

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.

A continent-wide study reveals clear relationships between regional abiotic conditions and post-dispersal seed predation

Aim Large-scale patterns linking energy availability, biological productivity and diversity form a central focus of ecology. Despite evidence that the activity and abundance of animals may be limited by climatic variables associated with regional biological productivity (e.g. mean annual precipitation and annual actual evapotranspiration), it is unclear whether plant–granivore interactions are themselves influenced by these climatic factors across broad spatial extents. We evaluated whether climatic conditions that are known to alter the abundance and activity of granivorous animals also affect rates of seed removal. Location Eleven sites across temperate North America. Methods We used a common protocol to assess the removal of the same seed species (Avena sativa) over a 2-day period. Model selection via the Akaike information criterion was used to determine a set of candidate binomial generalized linear mixed models that evaluated the relationship between local climatic data and post-dispersal seed predation. Results Annual actual evapotranspiration was the single best predictor of the proportion of seeds removed. Annual actual evapotranspiration and mean annual precipitation were both positively related to mean seed removal and were included in four and three of the top five models, respectively. Annual temperature range was also positively related to seed removal and was an explanatory variable in three of the top four models. Main conclusions Our work provides the first evidence that energy and precipitation, which are known to affect consumer abundance and activity, also translate to strong, predictable patterns of seed predation across a continent. More generally, these findings suggest that future changes in temperature and precipitation could have widespread consequences for plant species composition in grasslands, through impacts on plant recruitment.

Meta-analysis of prevalence

Meta-analysis is a method to obtain a weighted average of results from various studies. In addition to pooling effect sizes, meta-analysis can also be used to estimate disease frequencies, such as incidence and prevalence. In this article we present methods for the meta-analysis of prevalence. We discuss the logit and double arcsine transformations to stabilise the variance. We note the special situation of multiple category prevalence, and propose solutions to the problems that arise. We describe the implementation of these methods in the MetaXL software, and present a simulation study and the example of multiple sclerosis from the Global Burden of Disease 2010 project. We conclude that the double arcsine transformation is preferred over the logit, and that the MetaXL implementation of multiple category prevalence is an improvement in the methodology of the meta-analysis of prevalence.