When Hurricane Harvey landed along the Texas coast on August 25, 2017, it caused massive flooding and damage and displaced tens of thousands of residents of Harris County, Texas. Between August 29 and September 23, Harris County, along with community partners, operated a megashelter at NRG Center, which housed 3365 residents at its peak. Harris County Public Health conducted comprehensive public health surveillance and response at NRG, which comprised disease identification through daily medical record reviews, nightly “cot-to-cot” resident health surveys, and epidemiological consultations; messaging and communications; and implementation of control measures including stringent isolation and hygiene practices, vaccinations, and treatment. Despite the lengthy operation at the densely populated shelter, an early seasonal influenza A (H3) outbreak of 20 cases was quickly identified and confined. Influenza outbreaks in large evacuation shelters after a disaster pose a significant threat to populations already experiencing severe stressors. A holistic surveillance and response model, which consists of coordinated partnerships with onsite agencies, in-time epidemiological consultations, predesigned survey tools, trained staff, enhanced isolation and hygiene practices, and sufficient vaccines, is essential for effective disease identification and control. The lessons learned and successes achieved from this outbreak may serve for future disaster response settings. (Disaster Med Public Health Preparedness. 2019;13:97-101)

Objectives: Careful characterization of how functional decline co-evolves with cognitive decline in older adults has yet to be well described. Most models of neurodegenerative disease postulate that cognitive decline predates and potentially leads to declines in everyday functional abilities; however, there is mounting evidence that subtle decline in instrumental activities of daily living (IADLs) may be detectable in older individuals who are still cognitively normal. Methods: The present study examines how the relationship between change in cognition and change in IADLs are best characterized among older adults who participated in the ACTIVE trial. Neuropsychological and IADL data were analyzed for 2802 older adults who were cognitively normal at study baseline and followed for up to 10 years. Results: Findings demonstrate that subtle, self-perceived difficulties in performing IADLs preceded and predicted subsequent declines on cognitive tests of memory, reasoning, and speed of processing. Conclusions: Findings are consistent with a growing body of literature suggesting that subjective changes in everyday abilities can be associated with more precipitous decline on objective cognitive measures and the development of mild cognitive impairment and dementia. (JINS, 2018, 24, 104–112)

The life annuity business is heavily exposed to longevity risk. Risk transfer solutions are not yet fully developed, and when available they are expensive. A significant part of the risk must therefore be retained by the life insurer. So far, most of the research work on longevity risk has been mainly concerned with capital requirements and specific risk transfer solutions. However, the impact of longevity risk on shareholder value also deserves attention. While it is commonly accepted that a market-consistent valuation should be performed in this respect, the definition of a fair shareholder value for a life insurance business is not trivial. In this paper, we develop a multi-period market-consistent shareholder value model for a life annuity business. The model allows for systematic and idiosyncratic longevity risk and includes the most significant variables affecting shareholder value: the cost of capital (which in a market-consistent setting must be quantified in terms of frictional and agency costs, net of the value of the limited liability put option), policyholder demand elasticity and the cost of alternative longevity risk management solutions, namely indemnity-based and index-based solutions. We show how the model can be used for assessing the impact of different longevity risk management strategies on life insurer shareholder value and solvency.

This paper applies cointegration techniques, developed in econometrics to model long-run relationships, to cause-of-death data. We analyze the five main causes of death across five major countries, including USA, Japan, France, England & Wales and Australia. Our analysis provides a better understanding of the long-run equilibrium relationships between the five main causes of death, providing new insights into similarities and differences in trends. The results identify for the first time similarities between countries and genders that are consistent with past studies on the aging processes by biologists and demographers. The insights from biological theory on aging are found to be reflected in the cointegrating relations in all of the countries included in the study.

The analysis of causal mortality provides rich insight into changes in mortality trends that are hidden in population-level data. Therefore, we develop and apply a multinomial logistic framework to model causal mortality. We use internationally classified cause-of-death categories and data obtained from the World Health Organization. Inherent dependence amongst the competing causes is accounted for in the framework, which also allows us to investigate the effects of improvements in, or the elimination of, cause-specific mortality. This has applications to scenario-based forecasting often used to assess the impact of changes in mortality. The multinomial model is shown to be more conservative than commonly used approaches based on the force of mortality. We use the model to demonstrate the impact of cause-elimination on aggregate mortality using residual life expectancy and apply the model to a French case study.

This paper provides a detailed quantitative assessment of the impact of capital and default probability on product pricing and shareholder value for a life insurer providing life annuities. A multi-period cash flow model, allowing for stochastic mortality and asset returns, imperfectly elastic product demand, as well as frictional costs, is used to derive value-maximizing capital and pricing strategies for a range of one-year default probability levels reflecting differences in regulatory regimes including Solvency II. The model is calibrated using realistic assumptions. The sensitivity of results is assessed. The results show that value-maximizing life insurers should target higher solvency levels than the Solvency II regulatory one-year 99.5% probability under assumptions of reasonable levels of policyholder's aversion to insolvency risk. Even in the case of less restrictive solvency probabilities, policyholder price elasticity and solvency preferences are shown to be important factors for a life insurer's value-maximizing strategy.

In scattering specimens, multiphoton excitation and nondescanned detection improve imaging depth by a factor of 2 or more over confocal microscopy; however, imaging depth is still limited by scattering. We applied the concept of clearing to deep tissue imaging of highly scattering specimens. Clearing is a remarkably effective approach to improving image quality at depth using either confocal or multiphoton microscopy. Tissue clearing appears to eliminate the need for multiphoton excitation for deep tissue imaging.