Predicting Attitudes Towards Transactional Sex: The Interactive Relationship Between Gender and Attitudes on Sexual Behaviour

Introduction: This article explores explanations for attitudes towards the acceptability of transactional sex. The sparse research investigating attitudes towards transactional sex uncovers a link between gender equality, or feminism, and a lack of support for the trade in sex. However, there are no research agendas that attempt to explain variance in attitudes towards transactional sex where support for gender equality is widespread throughout a population. Methods: We estimate regression models utilizing the 2017 Danish Values Survey (Den Danske Værdiundersøgelse) in order to predict views on the acceptability of transactional sex. Results: While the trade in sex is legal in Denmark, we find that a majority of respondents hold negative attitudes towards transactional sex, which conveys a lack of congruence between public opinion and policy. Further, gender is a powerful predictor of attitudes towards transactional sex, with women finding it less acceptable. In addition, the analysis uncovers that general attitudes towards sexual behaviour are the largest predictor of views on the acceptability of transactional sex. That being said, we find that men’s views on the acceptability of transactional sex are more of a function of their general attitudes towards sexual behaviour than they are for women. Conclusions: The findings indicate that, unlike men, women appear to differentiate between their attitudes towards general sexual behaviour and their views on sexual behaviour that they may associate with negative societal implications

High-resolution spatiotemporal weather models for climate studies

<p>Abstract</p> <p>Background</p> <p>Climate may exert a strong influence on health, in particular on vector-borne infectious diseases whose vectors are intrinsically dependent on their environment. Although critical, linking climate variability to health outcomes is a difficult task. For some diseases in some areas, spatially and temporally explicit surveillance data are available, but comparable climate data usually are not. We utilize spatial models and limited weather observations in Puerto Rico to predict weather throughout the island on a scale compatible with the local dengue surveillance system.</p> <p>Results</p> <p>We predicted monthly mean maximum temperature, mean minimum temperature, and cumulative precipitation at a resolution of 1,000 meters. Average root mean squared error in cross-validation was 1.24°C for maximum temperature, 1.69°C for minimum temperature, and 62.2 millimeters for precipitation.</p> <p>Conclusion</p> <p>We present a methodology for efficient extrapolation of minimal weather observation data to a more meaningful geographical scale. This analysis will feed downstream studies of climatic effects on dengue transmission in Puerto Rico. Additionally, we utilize conditional simulation so that model error may be robustly passed to future analyses.</p

Gravity vs radiation model: on the importance of scale and heterogeneity in commuting flows

We test the recently introduced radiation model against the gravity model for the system composed of England and Wales, both for commuting patterns and for public transportation flows. The analysis is performed both at macroscopic scales, i.e. at the national scale, and at microscopic scales, i.e. at the city level. It is shown that the thermodynamic limit assumption for the original radiation model significantly underestimates the commuting flows for large cities. We then generalize the radiation model, introducing the correct normalisation factor for finite systems. We show that even if the gravity model has a better overall performance the parameter-free radiation model gives competitive results, especially for large scales.Comment: in press Phys. Rev. E, 201

Evaluation of Internet-Based Dengue Query Data: Google Dengue Trends

Dengue is a common and growing problem worldwide, with an estimated 70–140 million cases per year. Traditional, healthcare-based, government-implemented dengue surveillance is resource intensive and slow. As global Internet use has increased, novel, Internet-based disease monitoring tools have emerged. Google Dengue Trends (GDT) uses near real-time search query data to create an index of dengue incidence that is a linear proxy for traditional surveillance. Studies have shown that GDT correlates highly with dengue incidence in multiple countries on a large spatial scale. This study addresses the heterogeneity of GDT at smaller spatial scales, assessing its accuracy at the state-level in Mexico and identifying factors that are associated with its accuracy. We used Pearson correlation to estimate the association between GDT and traditional dengue surveillance data for Mexico at the national level and for 17 Mexican states. Nationally, GDT captured approximately 83% of the variability in reported cases over the 9 study years. The correlation between GDT and reported cases varied from state to state, capturing anywhere from 1% of the variability in Baja California to 88% in Chiapas, with higher accuracy in states with higher dengue average annual incidence. A model including annual average maximum temperature, precipitation, and their interaction accounted for 81% of the variability in GDT accuracy between states. This climate model was the best indicator of GDT accuracy, suggesting that GDT works best in areas with intense transmission, particularly where local climate is well suited for transmission. Internet accessibility (average ∼36%) did not appear to affect GDT accuracy. While GDT seems to be a less robust indicator of local transmission in areas of low incidence and unfavorable climate, it may indicate cases among travelers in those areas. Identifying the strengths and limitations of novel surveillance is critical for these types of data to be used to make public health decisions and forecasting models

Public health for the people: participatory infectious disease surveillance in the digital age

The 21st century has seen the rise of Internet-based participatory surveillance systems for infectious diseases. These systems capture voluntarily submitted symptom data from the general public and can aggregate and communicate that data in near real-time. We reviewed participatory surveillance systems currently running in 13 different countries. These systems have a growing evidence base showing a high degree of accuracy and increased sensitivity and timeliness relative to traditional healthcare-based systems. They have also proven useful for assessing risk factors, vaccine effectiveness, and patterns of healthcare utilization while being less expensive, more flexible, and more scalable than traditional systems. Nonetheless, they present important challenges including biases associated with the population that chooses to participate, difficulty in adjusting for confounders, and limited specificity because of reliance only on syndromic definitions of disease limits. Overall, participatory disease surveillance data provides unique disease information that is not available through traditional surveillance sources

Asking About “Prostitution”, “Sex Work” and “Transactional Sex”: Question Wording and Attitudes Toward Trading Sexual Services

This study explored the impact of question wording on attitudes toward trading sexual services. There are no previous research agendas investigating how views on the trade in sex are susceptible to question word choice. We utilized an original survey to assess how three different concepts used to represent the exchange of sexual services impact on the reported level of acceptability among respondents. The concepts we selected were “prostitution,” “sex work”, and “transactional sex.” We also explored the contrasting effects of predictor variables based on question wording, such as general sexual attitudes and positive and negative associations with trading sex. We found that attitudes toward the trading of sex is partially a function of question wording. Respondents were more likely to translate negative associations with the exchange of sexual services into unacceptability when assigned a question using the term prostitution. The results indicated that prostitution is a stigmatized word, resonating more negatively with respondents as compared to sex work or transactional sex. When asked about transactional sex, respondents were less likely to translate an attitude into an assessment on acceptability, indicating that the term is vague. Sex work elicited more neutral responses on acceptability. The results suggest that sex work would be the most useful term to use in dialogue and debate regarding the trade in sexual services. Sex work does not appear to be imbued with the same sense of negativity as prostitution and the meaning of sex work seems clearer than transactional sex.</p

A quantitative central limit theorem for linear statistics of random matrix eigenvalues

It is known that the fluctuations of suitable linear statistics of Haar distributed elements of the compact classical groups satisfy a central limit theorem. We show that if the corresponding test functions are sufficiently smooth, a rate of convergence of order almost $1/n$ can be obtained using a quantitative multivariate CLT for traces of powers that was recently proven using Stein's method of exchangeable pairs.Comment: Title modified; main result stated under slightly weaker conditions; accepted for publication in the Journal of Theoretical Probabilit

Evaluating the Performance of Infectious Disease Forecasts: A Comparison of Climate-Driven and Seasonal Dengue Forecasts for Mexico

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model

Fluctuation properties of the TASEP with periodic initial configuration

We consider the joint distributions of particle positions for the continuous time totally asymmetric simple exclusion process (TASEP). They are expressed as Fredholm determinants with a kernel defining a signed determinantal point process. We then consider certain periodic initial conditions and determine the kernel in the scaling limit. This result has been announced first in a letter by one of us and here we provide a self-contained derivation. Connections to last passage directed percolation and random matrices are also briefly discussed.Comment: 33 pages, 4 figure, LaTeX; We added several references to the general framework and techniques use

The Conservation Reserve Program: Economic Implications for Rural America

This report estimates the impact that high levels of enrollment in the Conservation Reserve Program (CRP) have had on economic trends in rural counties since the program's inception in 1985 until today. The results of a growth model and quasi-experimental control group analysis indicate no discernible impact by the CRP on aggregate county population trends. Aggregate employment growth may have slowed in some high-CRP counties, but only temporarily. High levels of CRP enrollment appear to have affected farm-related businesses over the long run, but growth in the number of other nonfarm businesses moderated CRP's impact on total employment. If CRP contracts had ended in 2001, simulation models suggest that roughly 51 percent of CRP land would have returned to crop production, and that spending on outdoor recreation would decrease by as much as $300 million per year in rural areas. The resulting impacts on employment and income vary widely among regions having similar CRP enrollments, depending upon local economic conditions.Community/Rural/Urban Development, Land Economics/Use,