Temperature, season, and latitude influence development-related phenotypes of Philippine Aedes aegypti (Linnaeus): Implications for dengue control amidst global warming.

BACKGROUND: Dengue is endemic in the Philippines. Aedes aegypti is the primary vector. This study aimed to determine the hatching behavior and viability of Ae. aegypti first-generation (F1) eggs when exposed to temperature and photoperiod regimes under laboratory conditions. METHODS: Parental eggs were collected from selected highland and lowland sites in the Philippine big islands (Luzon, Visayas, and Mindanao) during the wet (2017-2018) and dry (2018) seasons. F1 egg cohorts were exposed separately in environmental chambers at 18, 25, and 38 °C with respective photoperiods for 6 weeks. Phenotypes (percent pharate larvae [PPL], hatch rates [HRs], and reproductive outputs [ROs]) were determined. RESULTS: Results of multivariate analyses of variance (MANOVA) between seasons showed significant main effects of temperature, season, and big island on all phenotypes across all sites. Significant interaction effects between seasons on all phenotypes across sites were shown between or among (1) season and big island, (2) season and temperature, (3) big island and temperature, (4) season, big island, and temperature, (5) big island, altitude, and temperature, and (6) season, big island, altitude, and temperature. Factors associated with the big islands might include their ecology, available breeding sites, and day lengths due to latitudinal differences, although they were not measured in the field. MANOVA results within each season on all phenotypes across sites showed (1) significant main effects of big island and temperature, and (2) significant interaction effects between big island and temperature within the wet season and (3) between temperature and photoperiod within the dry season. PPL were highest at 18 °C and were formed even at 38 °C in both seasons. Pharate larvae might play an adaptive role in global warming, expanded distribution to highlands, and preponderance to transmit human diseases. HRs in both seasons were highest at 25 °C and lowest at 38 °C. ROs were highest at 25 °C in the wet season and at 18 °C in the dry season. CONCLUSIONS: Temperature and latitude of Philippine big islands influenced the development-related phenotypes of Ae. aegypti in both seasons. The two seasons influenced the phenotypes and their interaction effects with big island and/or temperature and/or altitude. Recommendations include year-round enhanced 4S control strategies for mosquito vectors and water pipeline installation in rural highlands.

Method to Improve the Effectiveness of Community-Based Participatory Research Projects: A Social Network Approach.

BACKGROUND: To successfully complete a community-based participatory research (CBPR) project, different types of relationships and different degrees of stakeholder investment are required, depending on context. We propose a method that will help assess relational connectivity and investment in many types of CBPR structures. Using data from a CBPR project, a network method is examined to test its effectiveness in identifying the CBPR project's strengths and weaknesses. METHODS: A social network method is proposed to increase CBPR effectiveness. This involves constructing various social networks related to the CBPR project: a current network, preferred networks for each collaborative stakeholder, and a projected network. These measure the state of the current network and provide a roadmap-via the stakeholder and projected networks-to what we believe may be an ideal CBPR network structure. RESULTS: Analyses indicated areas of the network where improvements could be made to expand collaboration. Network analysis revealed differing views of the preferred social network from various subgroups, indicating where modification of ties and perspectives of stakeholders could improve the collaboration. CONCLUSIONS: This social network method promotes analysis of CBPR factors and provides information for changes designed to improve CBPR collaborations and potentially lead to better outcomes.

Income inequality and mass shootings in the United States.

BACKGROUND: Mass shootings are an increasingly common phenomenon in the United States. However, there is little research on whether the recent growth of income inequality is associated with this rise of mass shootings. We thus build on our prior research to explore the connection between income inequality and mass shootings across counties in the United States. METHODS: We assemble a panel dataset of 3144 counties during the years 1990 to 2015. Socioeconomic data are extracted from the U.S. Bureau of the Census. Mass shootings data are from three databases that compile its information from the Federal Bureau of Investigation and media sources, respectively. These data are analyzed using random effects negative binomial regressions, while controlling for seven additional predictors of crime. RESULTS: Counties experiencing a one standard deviation growth of income inequality witnessed 0.43 more mass shootings when using the definition of three or more victim injuries (incidence rate ratio [IRR] = 1.43; 95% confidence interval [CI] = 1.24, 1.66; P < .001) and 0.57 more mass shootings when using the designation of four or more victim deaths (IRR = 1.57; 95% CI = 1.26, 1.96; P < .001). CONCLUSIONS: Counties with growing levels of income inequality are more likely to experience mass shootings. We assert that one possibility for this finding is that income inequality fosters an environment of anger and resentment that ultimately leads to violence.

Income Inequality, Household Income, and Mass Shooting in the United States.

Mass shootings are becoming a more common occurrence in the United States. Data show that mass shootings increased steadily over the past nearly 50 years. Crucial is that the wide-ranging adverse effects of mass shootings generate negative mental health outcomes on millions of Americans, including fear, anxiety, and ailments related to such afflictions. This study extends previous research that finds a strong positive relationship between income inequality and mass shootings by examining the effect of household income as well as the interaction between inequality and income. To conduct our analyses, we compile a panel dataset with information across 3,144 counties during the years 1990 to 2015. Mass shootings was measured using a broad definition of three or more victim injuries. Income inequality was calculated using the post-tax version of the Gini coefficient. Our results suggest that while inequality and income alone are both predictors of mass shootings, their impacts on mass shootings are stronger when combined via interaction. Specifically, the results indicate areas with the highest number of mass shootings are those that combine both high levels of inequality and high levels of income. Additionally, robustness checks incorporating various measures of mass shootings and alternative regression techniques had analogous results. Our findings suggest that to address the mass shootings epidemic at its core, it is essential to understand how to stem rising income inequality and the unstable environments that we argue are created by such inequality.