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Prolonging life is a global trend, and more medical expenditure is being spent on chronic diseases owing to population aging. Diseases commonly seen in middle-aged and elderly people, such as heart disease and diabetes, have slowed mortality improvement in recent years. Diabetes is a common chronic disease and comorbidity of many serious health conditions. The total estimated cost of diabetes in the United States was $327 billion in 2017. However, many people are unaware that diabetes is common, and at least 21.4% of adults do not know that they have diabetes. The number of diabetes-related deaths has been increasing, and diabetes was the 5th cause of death in Taiwan in 2019. In this study, we explore the trend and influence of diabetes in Taiwan and apply mortality models, such as the Lee-Carter and Age-Period-Cohort models, using data from Taiwan's National Insurance to model the incidence and mortality rates of diabetes. We found that the Lee-Carter model provides fairly satisfactory estimates and that people with diabetes regularly taking diabetes medication have lower mortality rates. Moreover, we demonstrate how these results can be used to design diabetes related insurance products and prepare the insured to face the impact of incurring diabetes. In addition, we consider different criteria for judging whether people have diabetes (as there is no consensus on these criteria) and investigate the issue of moral hazard in designing diabetes insurance products. [ABSTRACT FROM AUTHOR]

The purpose of this study is to describe the prevalence, trends, and associations of the use of outpatient prescription non-narcotic analgesics and proton pump inhibitors (PPIs) in Taiwan from 2010 to 2021. We identified the most commonly prescribed analgesics and PPIs, including paracetamol (acetaminophen) and nonsteroidal anti-inflammatory drugs (NSAIDs), in outpatient departments according to the data from Taiwan National Health Insurance Research Database and Taiwan's NHI Annual Statistical Report. This study shows that the traditional non-selective NSAIDs take the largest proportion of usage, representing over 60% of the annual usage of non-narcotic analgesics. Acetaminophen is the second and takes over 30% of annual usage. The selective COX-2 (coxibs) takes less than 10% of usage, but its use is growing rapidly. This study also shows that the annual usage of PPIs is growing rapidly and is not decreasing with the use of selective COX-2. The findings in this study can help us to focus on the most commonly prescribed drugs to investigate the prescription pattern of NSAIDs and PPIs, especially the combined use of selective COX-2 and PPIs, in future study. [ABSTRACT FROM AUTHOR]

It is often of interest to biologists to evaluate whether two populations are alike with respect to a similarity index; assessing the numbers of shared species is one way to do this. In this study, we propose two Turing-type estimators for the probability of discovering new shared species and two jackknife-type estimators for the number of shared species in two populations. We use computer simulation and empirical data analysis to evaluate the proposed approach. The jackknife-type estimators provide stable and reliable estimates, for both the probability of discovering new shared species and the number of shared species. We also compare the jackknife-type estimates with that of using sample coverage to estimate the number of shared species. The estimate of using sample coverage has better performance in the case of even populations, while the jackknife-type estimates have smaller bias in the case of unbalanced populations. When combined with a stopping rule based on the probability of observing new shared species, confidence intervals based on the proposed jackknife-type estimators can provide better coverage probability for the true number of shared species. The jackknife-type estimates can provide coverage probability close to 0.95 in all examples. [ABSTRACT FROM AUTHOR]

Background: Geographically weighted regression (GWR) is a modelling technique designed to deal with spatial non-stationarity, e.g., the mean values vary by locations. It has been widely used as a visualization tool to explore the patterns of spatial data. However, the GWR tends to produce unsmooth surfaces when the mean parameters have considerable variations, partly due to that all parameter estimates are derived from a fixed- range (bandwidth) of observations. In order to deal with the varying bandwidth problem, this paper proposes an alternative approach, namely Conditional geographically weighted regression (CGWR). Methods: The estimation of CGWR is based on an iterative procedure, analogy to the numerical optimization problem. Computer simulation, under realistic settings, is used to compare the performance between the traditional GWR, CGWR, and a local linear modification of GWR. Furthermore, this study also applies the CGWR to two empirical datasets for evaluating the model performance. The first dataset consists of disability status of Taiwan's elderly, along with some social-economic variables and the other is Ohio's crime dataset. Results: Under the positively correlated scenario, we found that the CGWR produces a better fit for the response surface. Both the computer simulation and empirical analysis support the proposed approach since it significantly reduces the bias and variance of data fitting. In addition, the response surface from the CGWR reviews local spatial characteristics according to the corresponded variables. Conclusions: As an explanatory tool for spatial data, producing accurate surface is essential in order to provide a first look at the data. Any distorted outcomes would likely mislead the following analysis. Since the CGWR can generate more accurate surface, it is more appropriate to use it exploring data that contain suspicious variables with varying characteristics. [ABSTRACT FROM AUTHOR]

Species diversity indices are designed to measure the species diversity of a community and to compare the species distribution structure of two communities. The Shannon and Simpson indices are for describing one community, and the Jaccard and Morisita indices are for comparing two communities. Only a few indices allow the simultaneous comparison of three or more communities. In this study, we propose a multiple-community similarity index based on a probabilistic approach, and compare it with other multiple-community indices. Empirical examples are considered as a demonstration of the proposed similarity indices. [ABSTRACT FROM AUTHOR]

Life expectancies of the human male and female have been increasing significantly since the turn of the 20th century, and the trend is expected to continue. The study of elderly mortality has thus become a favorite research topic. However, because there were not enough elderly data before 1990, there is still no conclusion about which mortality model is appropriate for describing.elderly mortality. In this study, we modify the regular discount sequence in the Bandit Problem and use it to describe elderly mortality. We found that many frequently used mortality models, such as the Gompertz Law, and famous mortality assumptions (Uniform Distribution of Death, Constant Force, and Hyperbolic assumption) all satisfy the requirement of a regular discount sequence. We also use empirical data from the HMD (Human Mortality Database from University of California, Berkeley), including data from Japan, the US, and Taiwan, to evaluate the proposed approach. The discount sequences of life expectancy and surviving number ratio do satisfy the regularity condition. In addition, we use the Brownian Motion Stochastic Differential Equation to model the discount sequence. Using this model, we predict the future mortality rates and life expectancy. The simulation study shows some promising results. [ABSTRACT FROM AUTHOR]

Population Projection is essential to policy planning, especially to social welfare. The cohort component method is the most popular method for population projection. The future trends of fertility, mortality, and immigration are often determined by the experts' opinions, which are also known as scenario forecasts, and then plugged into the cohort component method. However, the projections derived via the experts' opinions are deterministic and do not have implications in probability. To let the population projections possess the meaning of probability by renovating the scenario forecasts, researchers have developed three types of probabilistic forecasting methods, including the stochastic forecast method, random scenario method, and ex post method. In this paper, we study the block bootstrap method, a computer simulation method and also a stochastic forecast method, and evaluate the possibility of applying this method in population projection. Specifically, employing data from Taiwan, the U.S., Japan, and France, we use cross-validation and computer simulation to explore the limitations of the block bootstrap, and check if this method can produce reasonable projections. Based on the empirical results, we found that the block bootstrap is a feasible method and can produce stable population projections. In addition, we also study the ex post method proposed by Stoto (1983) and give the probability implications to projections from the Council for Economic Planning and Development (a scenario forecast). [ABSTRACT FROM AUTHOR]

Except in the U.S., the total fertility rates of developed countries have been smaller than the replacement level of 2.1. Without exception, the total fertility rates in Taiwan dropped to a historic low (1.2) in 2004 and are expected to be even smaller in 2006. In this study, we used the fertility data of four developed countries, Taiwan, Japan, the Netherlands and the USA (representing countries in Asia, Europe, and America), to fit the frequently used models (Gamma, Lee-Carter, Principal Component Analysis, Age-Group Fertility Rate, and Diffusion models) and determine which model has the best fit. We found that, for the total fertility rates, the best model in Taiwan, Japan, and the USA is our modified diffusion model, while in the Netherlands it is the age-group fertility rate model. For the age-specific fertility rates, the best model in Taiwan, Japan, and the Netherlands is the modified diffusion model, while in the USA it is the age-group fertility rate model. In addition, we use computer simulation to check the relative stability and relative efficiency of these fertility models. The age-group fertility rate model and the modified diffusion model also outperform other models. In conclusion, we recommend the readers use these two models. [ABSTRACT FROM AUTHOR]

Recently, according to information from Taiwan's National Health Insurance database, the medical expenditure on critical illness and injury has increased significantly because of an aging population. In this paper we are interested in investigating the impact of the aging population on the cost of critical illness and injury. In the first part of this research, we make use of Taiwan's National Health Insurance database from 1996 to 2001 to estimate the number of people suffering critical illness and injury. In particular, we apply actuarial methods and the method of cohort component to estimate outpatient medical expenditure of the critical illness and injury. The estimation shows that the projection of medical expenditure due to critical illness and injury grows linearly with time. This effect is consistent with the trend of an increase of numbers in elderly. It implies that an aging population will lead to a significant financial impact on Taiwan's national health insurance program. Furthermore, in the second part of this research, we construct a Markov model to calculate the expected premiums of critical illness and injury insurance under both social and commercial insurance systems. We then evaluate the required cost of critical illness and injuries in order to keep a financial balance for the Taiwan National Health Insurance program. [ABSTRACT FROM AUTHOR]

The total fertility rates in Taiwan area have been falling rapidly since 1960's, which reached a record low 1.4 in 2001, and trigger the population aging in Taiwan area. Possible causes attributing to this rapid decrease include more opportunities of higher education and employment for the female, which generally are treated as two of the main reasons for the delay of childbearing. However, the falling of fertility rates vary quite a lot in different age groups, and thus most well-known fertility models (such as Gamma and Lee-Carter models) fail to produce accurate predictions. In this paper, we shall extend the study of Huang and Yue (2002) and continue looking for acceptable fertility projection models. The models considered in this study include, in addition to traditional models for age-specific fertility rates, models for cohort fertility rate (CFR), age-and-parity specific birth rate, and diffusion. Data from 1949 to 1996 (or 1975 to 1996 for age-and-parity specific birth rate) are used as a pilot for verifying the model which has the best fit for data gathered from 1997 to 2001. We also consider the effect of Chinese zodiac to adjust the prediction values. We find that simpler models, such as individual group estimation, have smaller prediction errors comparing the complicated models such as the CFR model. [ABSTRACT FROM AUTHOR]