Implicit versus explicit vector management strategies in models for vector-borne disease epidemiology.

Throughout the vector-borne disease modeling literature, there exist two general frameworks for incorporating vector management strategies (e.g. area-wide adulticide spraying and larval source reduction campaigns) into vector population models, namely, the "implicit" and "explicit" control frameworks. The more simplistic "implicit" framework facilitates derivation of mathematically rigorous results on disease suppression and optimal control, but the biological connection of these results to real-world "explicit" control actions that could guide specific management actions is vague at best. Here, we formally define a biological and mathematical relationship between implicit and explicit control, and we provide mathematical expressions relating the strength of implicit control to management-relevant properties of explicit control for four common intervention strategies. These expressions allow the optimal control and basic reproduction number analyses typically utilized in implicit control modeling to be interpreted directly in terms of real-world actions and real-world monetary costs. Our methods reveal that only certain sub-classes of explicit control protocols are able to be represented as implicit controls, and that implicit control is a meaningful approximation of explicit control only when resonance-like synergistic effects between multiple explicit controls have negligible effects on population reduction. When non-negligible synergy exists, implicit control results, despite their mathematical tidiness, fail to provide accurate predictions regarding vector control and disease spread. Collectively, these elements build an effective bridge between analytically interesting and mathematically tractable implicit control and the challenging, action-oriented explicit control.

Physical and mental health perspectives of first year undergraduate rural university students.

BACKGROUND: University students are often perceived to have a privileged position in society and considered immune to ill-health and disability. There is growing evidence that a sizeable proportion experience poor physical health, and that the prevalence of psychological disorders is higher in university students than their community peers. This study examined the physical and mental health issues for first year Australian rural university students and their perception of access to available health and support services. METHODS: Cross-sectional study design using an online survey form based on the Adolescent Screening Questionnaire modeled on the internationally recognised HEADSS survey tool. The target audience was all first-year undergraduate students enrolled in an on-campus degree program. The response rate was 41% comprising 355 students (244 females, 111 males). Data was analysed using standard statistical techniques including descriptive and inferential statistics; and thematic analysis of the open-ended responses. RESULTS: The mean age of the respondents was 20.2 years (SD 4.8). The majority of the students lived in on-campus residential college style accommodation, and a third combined part-time paid work with full-time study. Most students reported being in good physical health. However, on average two health conditions were reported over the past six months, with the most common being fatigue (56%), frequent headaches (26%) and allergies (24%). Mental health problems included anxiety (25%), coping difficulties (19.7%) and diagnosed depression (8%). Most respondents reported adequate access to medical doctors and support services for themselves (82%) and friends (78%). However the qualitative comments highlighted concerns about stigma, privacy and anonymity in seeking counselling. CONCLUSIONS: The present study adds to the limited literature of physical and mental health issues as well as barriers to service utilization by rural university students. It provides useful baseline data for the development of customised support programs at rural campuses. Future research using a longitudinal research design and multi-site studies are recommended to facilitate a deeper understanding of health issues affecting rural university students.

The nature and cross-domain correlates of subjective age in the oldest old: Evidence from the OCTO Study.

Self-reflections of age and aging are predictors for key outcomes such as mortality, but little is known about the nature and potential antecedents of subjective age in very old age. We used cross-sectional data from the Swedish OCTO study (N = 267; B. Johansson & S. H. Zarit, 1995) and found that almost two thirds of the 84- to 90-year-olds reported not feeling old. Multinomial logistic regression analyses indicated that younger age and better physical functioning as well as higher well-being and mastery beliefs were all related to not feeling old. In multivariate analyses, however, mastery beliefs emerged as the most consistent and robust predictor of subjective age. Our findings suggest that adaptive capacities may be preserved into advanced age and highlight the pivotal role of perceived control for successful aging.