Mortality dynamics and the statutory retirement age proposal: an actuarial view

Storm tide (combination of storm surge and the astronomical tide) flooding is a natural hazard with significant global social and economic consequences. For this reason, government agencies and stakeholders need storm tide flood maps to determine population and infrastructure at risk to present and future levels of inundation. Computer models of varying complexity are able to produce regional-scale storm tide flood maps and current model types are either static or dynamic in their implementation. Static models of storm tide utilize storm tide heights to inundate locations hydrologically connected to the coast, whilst dynamic models simulate physical processes that cause flooding. Static models have been used in regional-scale storm tide flood impact assessments, but model limitations and coarse spatial resolutions contribute to uncertain impact estimates. Dynamic models are better at estimating flooding and impact but are computationally expensive. In this study we have developed a dynamic reduced-complexity model of storm tide flooding that is computationally efficient and is applied at hyper-resolutions (<100 m cell size) over regional scales. We test the performance of this dynamic reduced-complexity model and a separate static model at three test sites where storm tide observational data are available. Additionally, we perform a flood impact assessment at each site using the dynamic reduced-complexity and static model outputs. Our results show that static models can overestimate observed flood areas up to 204 % and estimate more than twice the number of people, infrastructure, and agricultural land affected by flooding. Overall we find that that a reduced-complexity dynamic model of storm tide provides more conservative estimates of coastal flooding and impact.

This study analyzed differences in healthy life expectancy in the elderly based on three health dimensions in Brazil from 1998 to 2008: disability-free life expectancy, healthy life expectancy based on self-rated health, and chronic disease-free life expectancy. The Sullivan method was used, combining life tables from the Brazilian Institute of Geography and Statistics (IBGE) and interval estimates of the prevalence of functional disability, self-rated health, and chronic diseases according to the Brazilian National Household Sample Survey (PNAD, 1998 and 2008). Besides the increase in life expectancy, the study showed significant and similar increases in disability-free life expectancy and healthy life expectancy based on self-rated health at almost all ages. Women had higher life expectancies than men, but expected to live longer with poor health, regardless of the indicator used to measure health. Although the studies measured health differently (making comparisons difficult), women showed a consistent disadvantage in healthy life expectancy.

In this paper, we discuss the relation between liquidity and the business cycle.There are some reasons that we should link liquidity with the business cycle. The“flight to liquidity” or “flight to quality” hypothesis might be the most important one. It is more likely that an investor shifts his portfolio from those stocks with lower liquidity or higher risk when the economy is in a recession.Instead of business cycle measures, we use the NBER recession signal as the proxy of real economy. We apply dynamic and static probit model to monthly liquidity proxy, because the NBER recession is not always available in current period. Besides we use Amihud (2002) ILR as our liquidity proxy.The dynamic probit model with liquidity added does not result in a significance improvement on prediction. No matter in sample or out of sample, the pure dynamic probit model performs well with itself history only. However, as we mentioned earlier, NBER recession signal is usually announced well until the recession has happened for months, leading to an infeasibility to implement dynamic model. On the other hand, liquidity plays an important role in static probit model. We find a robust positive relation between liquidity and recession. Adding liquidity as a predictor in the static model would significantly improve the forecast accuracy in th short horizon. This observation remains robust with comparison to other financial variables ( e.g. term spread, credit spread, market volatility, and excess return). We also find the existence of “size effect” that small firm is more sensitive to future recession. Our finding above is consistent with our claims of “flight to liquidity; that is, it is more likely for market participants to shift his portfolio from those stock with lower liquidity or higher risk in advance when the economy would be in a recession. The primary contribution of this paper is that liquidity proxy, Amihud’s liquidity proxy here, does provide useful information when predicting the future recession. We find that the liquidity proxy performs well especially in a short prediction horizon. Besides, the liquidity of small firms is more informative in prediction.

Cancer is a major cause of death and morbidity in China. We aimed to analyze the trends in cancer mortality in six provinces in northwest China (Xinjiang Uyghur Autonomous Region, Qinghai Province, Shaanxi Province, Gansu Province, Ningxia Hui Autonomous Region, and Inner Mongolia Autonomous Region) from 2013 to 2021 and to explore the effect of cancer on life expectancy and the years of life lost. Based on cancer mortality data and demographic data from national surveillance units in six northwestern provinces from 2013 to 2021 in the National Cause-of-Death Surveillance, we calculated crude mortality rates, age-standardized mortality rates, life expectancy, cause-eliminated life expectancy, potential gains in life expectancy (PGLEs), years of life lost (YLL), YLL rates, and average years of life lost. Joinpoint software was used to calculate the average annual percentage change and annual percentage change (APC) for cancer mortality. Arriaga’s decomposition method was used to estimate the contribution of cancer to life expectancy in each age group. The age-standardized mortality rate for cancer in the six provinces in northwest China was stable overall from 2013 to 2021 but exhibited a decreasing trend from 2017 to 2021 (APC = − 5.64%, p = 0.047), male cancer age-standardized mortality rates were consistently higher than those of women and plateaued, women exhibited a decreasing trend after 2017 (APC = − 5.56%, p = 0.032), and the standardized mortality rate was higher and stable for those aged ≥ 65 years. Compared with the study area population in 2013, that in 2021 increased by 1.01 years in life expectancy, and changes in cancer mortality contributed positively to the increase in life expectancy, contributing 0.217 years or 21.38% of the increase in life expectancy. The greatest positive effect was in the 0–1 year age group (0.066 years, 6.48%), and the greatest negative effect was in the ≥ 85 years age group (− 0.026 years, − 2.56%). At a 1.06-year increase in cancer cause-eliminated life expectancy, PGLEs increased by 0.047 years, with the highest increase in PGLEs in urban areas (0.091 years) and an upward trend in YLL rates in the study area population from 2013 to 2021 (average annual percentage change = 2.16, p = 0.001). Average years of life lost presented a stable trend. Cancer age-standardized mortality rate in the study region from 2013 to 2021 was stable overall but has exhibited a decline in recent years. However, the disease burden of YLL has continued to increase. Preventive interventions targeting male groups and the elderly population need to be strengthened.

In digitally controlled DC-DC converters with a single voltage feedback loop, the two quantizers, namely the A/D converter and the digital pulse-width modulator (DPWM), can cause undesirable limit-cycle oscillations. In this paper, static and dynamic models that include the quantization effects are derived and used to explain the origins of limit-cycle oscillations. In the static model, existence of DC solution, which is a necessary no-limit-cycle condition, is examined using a graphical method. A concept of amplitude and offset dependent gain is introduced to extend the describing function method and derive the dynamic system model. From the static and dynamic models, no-limit-cycle conditions associated with A/D, DPWM and compensator design criteria are derived. The conclusions are illustrated by simulation and experimental examples.

In digitally controlled dc-dc converters with a single voltage feedback loop, the two quantizers, namely the analog-to-digital (A/D) converter and the digital pulse-width modulator (DPWM), can cause undesirable limit-cycle oscillations. In this paper, static and dynamic models that include the quantization effects are derived and used to explain the origins of limit-cycle oscillations. In the static model, existence of dc solution, which is a necessary no-limit-cycle condition, is examined using a graphical method. Based on the generalized describing function method, the amplitude and offset-dependent gain model of a quantizer is applied to derive the dynamic system model. From the static and dynamic models, no-limit-cycle conditions associated with A/D, DPWM and compensator design criteria are derived. The conclusions are illustrated by simulation and experimental examples

A review of 17 gerontology textbooks currently available in the US indicated the existence in 7 texts of errors regarding the increase in life expectancy. Misconceptions concerning life expectancy appeared in several forms: increase in life expectancy at birth as indicating extension of the life span estimate for life expectancy at birth as the time when most people die life expectancy as an indicator of the end of the life span the aging of the population as a result of increases in life expectancy at birth and a failure to distinguish life expectancy at birth from expectancies at other ages. The increase in life expectancy at birth in this century should be interpreted as largely due to increased infant survival. Moreover only a small part of the increase in the proportion of elderly in the US is a result of the increase in life expectancy. Rather this increase is largely due to a general decline in fertility. The elderly today can expect to live longer lives than those at the beginning of this century but this increase is not nearly as dramatic as the increase in life expectancy at birth might suggest. It is important that the perpetuation of these myths which can lead to erroneous theory be halted.

As the main cause of large blackouts, understanding the complex interactions involved in cascading failure propagation is a critical area of ongoing research. The static model of cascading failures is a well-accepted method to investigate the cascading effects in power systems. However, as the power system evolves, dynamic behaviours become more variable, it is necessary to evaluate the validity of the assumptions in the static model. This paper compares and validates a conventional static model with a novel dynamic model capturing frequency dynamics. N-2 contingency analysis is used to trigger cascading events and the unserved demand and the number of branch outages are used as metrics. Analysis using the 39-bus, 200-bus and 2000-bus benchmark systems reveal similarities and differences between the static and dynamic models and reveal, for the first time, how these differences vary as network size grows. Sensitivity analysis on system dispatch capabilities provides some initial guidance to help reduce the differences between models and helps with understanding of the relative importance of cascading mechanisms.

Recent research has documented increasing education inequality in life expectancy among U.S. adults; however, much is unknown about other health status changes. The objective of study is to assess how healthy and unhealthy life expectancies, as classified by common chronic diseases, has changed for older adults across education groups. Data come from the Health and Retirement Study and National Vital Statistics. We created prevalence-based life tables using the Sullivan method to assess sex-specific life expectancies for stroke, heart disease, cancer, and arthritis by education group. In general, unhealthy life expectancy increased with each condition across education groups. However, the increases in unhealthy life expectancy varied greatly. While stroke increased by half a year across education groups, life expectancy with diabetes increased by 3 to 4 years. In contrast, the evidence for healthy life expectancy provides mixed results. Across chronic diseases, healthy life expectancy decreased by 1 to 3 years for respondents without a 4-year degree. Conversely, healthy life expectancy increased for the college educated by .5 to 3 years. While previous research shows increases in life expectancy for the most educated, trends in life expectancy with chronic conditions is less positive: not all additional years are in lived in good health. In addition to documenting life expectancy changes across education groups, research assessing health of older adults should consider the changing inequality across a variety of health conditions, which will have broad implications for population aging and policy intervention.

Integration of dynamic and static modeling on guided inquiry learning can help students to understand chemical concepts at macroscopic, symbolic and microscopic levels. This research aimed to examine and to explain the differences of students’ multiple representation ability that was taught by guided inquiry with dynamic and static modeling in redox reaction concept. This research used descriptive and quasi-experimental design. Data were analyzed using descriptive and manova statistics analysis. The results showed that the ability of students’ macroscopic level that was taught by guided inquiry with dynamic and static modeling is the same. While the student’s symbolic and microscopic level ability that was taught by guided inquiry with dynamic modeling higher than student’s ability that was taught by guided inquiry with static modeling. The implication of this research is chemistry learning with dynamic modeling can help students to construct chemical concept more easily and gain the complete understanding.

BackgroundThis paper compares cost-effectiveness results from two models of maternal immunization to prevent pertussis in infants in Brazil, one static, one dynamic, to explore when static models are adequate for public health decisions and when the extra effort required by dynamic models is worthwhile. MethodsWe defined two scenarios to explore key differences between static and dynamic models, herd immunity and time horizon. Scenario 1 evaluates the incremental cost/DALY of maternal acellular pertussis (aP) immunization as routine infant vaccination coverage ranges from low/moderate up to, and above, the threshold at which herd immunity begins to eliminate pertussis. Scenario 2 compares cost-effectiveness estimates over the models’ different time horizons. Maternal vaccine prices of $9.55/dose (base case) and $1/dose were evaluated. ResultsThe dynamic model shows that maternal immunization could be cost-saving as well as life-saving at low levels of infant vaccination coverage. When infant coverage reaches the threshold range (90–95%), it is expensive: the dynamic model estimates that maternal immunization costs $2 million/DALY at infant coverage > 95% and maternal vaccine price of $9.55/dose; at $1/dose, cost/DALY is $200,000. By contrast, the static model estimates costs/DALY only modestly higher at high than at low infant coverage. When the models’ estimates over their different time horizons are compared at infant coverage < 90–95%, their projections fall in the same range. ConclusionsStatic models may serve to explore an intervention’s cost-effectiveness against infectious disease: the direction and principal drivers of change were the same in both models. When, however, an intervention too small to have significant herd immunity effects itself, such as maternal aP immunization, takes place against a background of vaccination in the rest of the population, a dynamic model is crucial to accurate estimates of cost-effectiveness. This finding is particularly important in the context of widely varying routine infant vaccination rates globally. Clinical Trial registryClinical Trial registry name and registration number: Not applicable.

PurposeContinuum robots modeling, be it from a hard or soft class, is giving rise to several challenges compared with rigid robots. These challenges are mainly due to kinematic redundancy, dynamic nonlinearity and high flexibility. This paper aims initially at designing a hard class of continuum robots, namely, cable-driven continuum robot (CDCR) and equally at developing their kinematic and dynamic models.Design/methodology/approachFirst, the CDCR prototype is constructed, and its description is made. Second, kinematic models are established based on the constant curvature assumption and inextensible bending section. Third, by using the Lagrange method, the dynamic model is derived under some simplifications and based on the kinematic equations, in which the flexible backbone’s elasticity modulus was identified experimentally. Finally, the static model of the CDCR is also derived based on the dynamic model.FindingsNumerical examples are carried out using Matlab software to verify the static and dynamic models. Moreover, the static model is validated by comparing the simulation’s results to the real measurements that have been provided with satisfactory results.Originality/valueTo reduce the complexity of the dynamic model’s expressions and avoid the numerical singularity when the bending angle is close to zero, some simplifications have been taken, especially for the kinetic energy terms, by using the nonlinear functions approximation. Hence, the main advantage of this analytical-approximate solution is that it can be applied in the bending angle that ranges up to 2p with reasonable errors, unlike the previously proposed techniques. Furthermore, the resulting dynamic model has, to some extent, the proprieties of simplicity, accuracy and fast computation time. Ultimately, the obtained results from the simulations and real measurements demonstrate that the considered CDCR’s static and dynamic models are feasible.

Maximizing the Effectiveness of Integrated Reservoir Studies: Practical Approaches to Improving the Process and Results

Prior work has highlighted increases in life expectancy in the USA during the Great Depression. This contradicts the tenet that life expectancy is positively correlated with human welfare, but it coheres with recent literature on mortality and recessions. We construct Lee-Carter interval estimates of life expectancy during the Great Depression, based on trends before 1929. In this analysis, all-race life expectancy did not grow unusually during the Great Depression. However, nonwhites did see greater-than-expected increases in life expectancy in 1930–1933. We discuss potential explanations. We conclude by urging scholars of mortality during this time period to focus on race whenever the data permit it.

Modeling travelers’ mode choice behavior is an important component of travel demand studies. In an effort to account for day-to-day dynamics of travelers’ mode choice behavior, the current study develops a dynamic random effects logit model to endogenously incorporate the mode chosen for a day into the utility function of the mode chosen for the following day. A static multinomial logit model is also estimated to examine the performance of the dynamic model. Per the results, the dynamic random effects model outperforms the static model in relation to predictive power. According to the accuracy indices, the dynamic random effects model offers the predictive power of 60.0% for members of car-deficient households, whereas the static model is limited to 43.1%. Also, comparison of F1-scores indicates that the predictive power of the dynamic random effects model with respect to active travels is 47.1% whereas that of the static model is as low as 15.0%. The results indicate a significant day-to-day dynamic behavior of transit users and active travelers. This pattern is found to be true in general, but not for members of car-deficient households, who are found more likely to choose the same mode for two successive days.