Development of a Pilot Literacy Scale to Assess Knowledge, Attitudes, and Behaviors towards Climate Change and Infectious Disease Dynamics in Suriname

Prior research has shown that climate literacy is sparse among low- and middle-income countries. Additionally, no standardized questionnaire exists for researchers to measure climate literacy among general populations, particularly with regards to climate change effects on vector-borne diseases (VBDs). We developed a comprehensive literacy scale to assess current knowledge, attitudes, and behaviors towards climate change and VBD dynamics among women enrolled in the Caribbean Consortium for Research in Environmental and Occupational Health (CCREOH) cohort in Suriname. Items were generated by our research team and reviewed by a group of six external climate and health experts. After the expert review, a total of 31 climate change and 21 infectious disease items were retained. We estimated our sample size at a 10:1 ratio of participants to items for each scale. In total, 301 women were surveyed. We validated our scales through exploratory (n = 180) and confirmatory factor analyses (n = 121). An exploratory factor analysis for our general Climate Change Scale provided a four-construct solution of 11 items. Our chi-squared value (X2 = 74.32; p = 0.136) indicated that four factors were sufficient. A confirmatory factor analysis reinforced our findings, providing a good model fit (X2 = 39.03; p = 0.23; RMSEA = 0.015). Our Infectious Disease Scale gave a four-construct solution of nine items (X2 = 153.86; p = 0.094). A confirmatory factor analysis confirmed these results, with a chi-squared value of 19.16 (p = 0.575) and an RMSEA of 0.00. This research is vitally important for furthering climate and health education, especially with increases in VBDs spread by Aedes mosquitoes in the Caribbean, South America, and parts of the southern United States

Municipality Level Dengue Risk Prediction Modeling in Brazil and its Impacts for Future Public Health Interventions

Current prediction models for dengue risk are restricted to country-wide estimates or are insufficient at accounting for localized variations in outbreak risk. These models focus primarily on climate and large-scale factors that may not reflect true risk across municipalities or neighborhoods, and do not account for other determinants of health that have also been previously correlated with risk of dengue. We hypothesized that widespread municipality-level dengue outbreak forecasting would have the potential to better capture small-scale transmission dynamics and provide more accurate local outbreak predictions. We built several boosted regression tree (BRT) models to predict outbreak risk for 167 municipalities in Pernambuco, Brazil. Using training data from 2010-2015, we utilized climate, prior surveillance data, and social and environmental indicators of health as features for classification of regions as low or high risk for dengue in 2016. Our best model included real-time temperature and precipitation, lagged climate effects and prior surveillance data. This model gave a training AUC of 0.963 and a testing AUC of 0.939, with a total of 1,842 correct observations from 2,004 predictions. Additionally, this model successfully predicted 74.8% of high risk classifications, a marked improvement from previous iterations. Quantification of predictor associations through univariate and multivariate regression analyses revealed correlations fairly consistent with our BRT results. In general, we saw that inclusion of most socio-environmental predictors had minor influence over BRT predictions, and were not statistically significant in correlation with increased dengue risk when compared to other predictors. We can conclude from this dissertation research that a BRT approach is effective for modeling dengue transmission dynamics and can successfully predict high risk dengue regions using relevant climatic factors as well as prior case data, though more research is needed to establish strong socio-environmental patterns with small-scale dengue outbreak risk. Predictive models can serve as in-depth complementary tools to current dengue surveillance systems by providing useful information on upcoming outbreaks and by estimating where most cases are most likely to occur prior to peak transmission. Further development of these models can also provide the insight necessary to restructure current vector control policies and strengthen existing dengue intervention practices

Mechanisms that Regulate Skin Resistance to Water Loss Rates in Plethodon Salamanders Across Various Body Sizes

The ecology and evolution of organisms can often be explained by specific mechanisms involving molecules, genes, and cellular characteristics. Being lungless, salamanders require moist skin to breathe. By maintaining moist skin, salamanders lose water to their environment, and consequently, the amount of time they can forage is determined by the rate at which salamanders lose water to their environment. Salamanders with high skin resistances (and thus lower water loss rates) can be active for longer periods, potentially increasing their fitness. In lungless salamanders, adults have a distinct advantage over juveniles due to their higher skin resistance to water loss. However, the mechanism by which adult salamanders have higher skin resistances than juveniles remains an unanswered question. Here, we use a variety of histological and lipid assays to determine differences between skin structure and skin secretion composition between large and small bodied salamanders. Skin secretions were collected from live individuals, stained with Sudan Black B, and analyzed using ImageJ to characterize lipid content. Skin samples were also stained with hematoxyline-eosin solution to investigate skin morphology. The results indicated that lipids may play an important role in determining skin resistance of small salamanders. The histological assays indicate dramatic differences in skin morphology across body sizes, with larger salamanders having much thicker skin.Therefore, the interaction between skin morphology and physiological responses will determine how salamanders respond to their environment

A scoping review of current climate change and vector-borne disease literacy and implications for public health interventions

Climate literacy assesses general understanding of climate, climate change, and its effects on the environment as well as human health. Despite vast scientific evidence to support climate change and its associated consequences, particularly with regards to vector-borne diseases, climate change knowledge, attitudes, and behaviors among the general population is relatively poor. In this study, we conducted a thorough review of the current literature to evaluate the scope of global climate and health literacy studies and identify key areas for improvement. We found that very few climate and health literacy studies were based in low- and middle-income countries, and those that were did not make mention of significant regional climate change impacts and specifically those that increase mosquito-borne disease transmission in high-risk areas. We also noted that of the twenty-three studies included in our final review, most focused their assessments on general climate and climate change knowledge, and not on literacy of the relationships between climate change and environmental impacts or subsequent health outcomes. Our findings make it clear that moving forward, there is a major need for climate and health literacy research to expand upon existing climate literature to include additional assessments of the relationships between certain climate change impacts and infectious diseases in particular, as well as to make available a more comprehensive overview of climate and health information to the public in the future

Inter-rater reliability of the infectious disease modeling reproducibility checklist (IDMRC) as applied to COVID-19 computational modeling research

Abstract Background Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications. Methods Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 30th, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss’ kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated. Results Questions related to the computational environment (mean κ = 0.90, range = 0.90–0.90), analytical software (mean κ = 0.74, range = 0.68–0.82), model description (mean κ = 0.71, range = 0.58–0.84), model implementation (mean κ = 0.68, range = 0.39–0.86), and experimental protocol (mean κ = 0.63, range = 0.58–0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23–0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation. Conclusions: The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater reliability assessment found that most scores were characterized by moderate or greater agreement. These results suggest that the IDMRC might be used to provide reliable assessments of the potential for reproducibility of published infectious disease modeling publications. Results of this evaluation identified opportunities for improvement to the model implementation and data questions that can further improve the reliability of the checklist