Coordinating Qualitative Predictor Variables in an Applied Linear Model: Analysis and Application for Applied Sciences.

Background In applied sciences, statistical models are pivotal for uncovering relationships in complex datasets. The applied linear model establishes associative links between variables. While qualitative predictors are essential, their integration into linear models poses challenges. The dummy variable approach transforms qualitative variables into binary ones for regression analysis. Multilayer Feedforward Neural Networks (MLFFNN) offer validation of regression models, and fuzzy regression offers alternative methods to address the ambiguity of qualitative predictors. This study aims to enhance the integration of qualitative predictors in applied linear models through statistical methodologies. Material and methods This study design involves the transformation of qualitative predictors into dummy variables, the bootstrapping technique to improve the parameter estimates, the Multilayer Feedforward Neural Network, and fuzzy regression. This study uses the programming language R as an analysis tool. Results The multiple linear regression model demonstrates precision and a significant fit (p<0.05), with an R-squared value of 0.95 and mean square error (MSE) of 9.97. Comparing actual and predicted values, fuzzy regression exhibits superior predictability over linear regression. The MLFFNN yields a reduced MSE net of 0.362, indicating enhanced prediction precision for derived models. Conclusion This study presents a precise methodology for integrating qualitative variables into linear regression, supported by the combination of specific statistical methodologies to enhance predictive modeling. By integrating fuzzy linear regression, MLFF neural networks, and bootstrapping, the proposed technique emerges as the most effective approach for modeling and prediction. These findings underscore the efficacy of this method in seamlessly integrating qualitative variables into linear models, ultimately enhancing accuracy and prediction capabilities.

The effects of parental retirement on adult children's health: Evidence from China.

Using representative data from China, we examine the causal effects of parental retirement on the health of adult children. To do so, we adopt a fuzzy regression discontinuity design and exploit the mandatory retirement ages in China as cut-off points. We find no evidence that parental retirement has significant effects on the mental health, healthcare utilization, or risky health behaviors of adult children. However, paternal retirement and maternal retirement have different effects on adult children's Self-reported health (SRH). Paternal retirement has a significantly negative effect only on the SRH of sons, while maternal retirement does not induce such effects. Potential mechanisms of intergenerational transfer through which parental retirement might affect adult children's health are also explored.

Chemical composition of arsenic-based acid mine drainage in the downstream of a gold mine: Fuzzy regression and clustering analysis.

This study employs fuzzy regression and fuzzy multivariate clustering techniques to analyze arsenic-polluted water samples originating from acid rock drainage in waste rock dumps. The research focuses on understanding the complex relationships between variables associated with arsenic contamination, such as water arsenic concentration, pH levels, and soil characteristics. To this end, fuzzy regression models were developed to estimate the relationships between water arsenic concentration and independent variables, thus, incorporating the inherent uncertainties into the analysis. Furthermore, multivariate fuzzy k-means clustering analysis facilitated the identification of fuzzy-based clusters within the dataset, providing insights into spatial patterns and potential sources of arsenic pollution. The pairwise comparisons indicated the strongest correlation of 0.62 between soil total arsenic and pH, while the weakest correlation of 0.13 was observed between soil-soluble arsenic and soil iron, providing valuable insights into their relationships and impact on water arsenic levels. The associated uncertainties in the relationships among the variables were determined based on the degree of belongingness of each data point to various fuzzy sets. Three distinct clusters emerged from the analysis: Cluster 1 comprised Points 5, 6, and 7; Cluster 2 included Points 1, 2, 3, 4, 8, and 9; and Cluster 3 consisted of Points 10, 11, 12, and 13. The findings enhance our understanding of the factors influencing arsenic contamination to provide an effective mitigation strategy in acid rock drainage scenarios. This research also demonstrates the applicability and effectiveness of fuzzy regression and fuzzy multivariate clustering in the analysis of arsenic-polluted water samples.

Sick and depressed? The causal impact of a diabetes diagnosis on depression.

BACKGROUND: There is sparse evidence on the impact of health information on mental health as well as on the mechanisms governing this relationship. We estimate the causal impact of health information on mental health via the effect of a diabetes diagnosis on depression. METHODS: We employ a fuzzy regression discontinuity design (RDD) exploiting the exogenous cut-off value of a biomarker used to diagnose type-2 diabetes (glycated haemoglobin, HbA1c) and information on psycometrically validated measures of diagnosed clinical depression drawn from rich administrative longitudinal individual-level data from a large municipality in Spain. This approach allows estimating the causal impact of a type-2 diabetes diagnosis on clinica ldepression. RESULTS: We find that overall a type-2 diabetes diagnosis increases the probability of becoming depressed, however this effect appears to be driven mostly by women, and in particular those who are relatively younger and obese. Results also appear to differ by changes in lifestyle induced by the diabetes diagnosis: while women who did not lose weight are more likely to develop depression, men who did lose weight present a reduced probability of being depressed. Results are robust to alternative parametric and non-parametric specifications and placebo tests. CONCLUSIONS: The study provides novel empirical evidence on the causal impact of health information on mental health, shedding light on gender-based differences in such effects and potential mechanisms through changes in lifestyle behaviours.

Effect of retirement on medical reimbursement expenses-evidence from China.

BACKGROUND: Medical reimbursement in China is not for all diseases, and medical reimbursement expenses are not completely consistent with medical service demand, though the scope and proportion of medical reimbursement are gradually expanding. This study aimed to examine the effect of retirement on medical reimbursement expenses in urban China. METHODS: The effect of retirement on medical reimbursement expenses were estimated by using fuzzy regression discontinuity based on data from the China Health and Retirement Longitudinal Study (CHARLS) in 2011, 2013, 2015, and 2018. Its group heterogeneity by educational backgrounds and marital status, and underlying mechanisms were also explored. RESULTS: Retirement increased medical reimbursement expenses of outpatient significantly (P < 0.05).Low time cost and deteriorating health status after retirement were possible mechanisms in this association. Retirement increased the reimbursement expenses significantly among the older adults with more educational obtainment or being widowed/divorced. CONCLUSION: The above findings indicated that there was a positive association between retirement and medical reimbursement expenses. The scope and proportion of medical reimbursement should be incorporated into retirement policy for improving medical service accessibility and rational healthcare utilization of retired population.

Short-term effect of retirement on health: Evidence from nonparametric fuzzy regression discontinuity design.

We estimate the short-term effect of retirement on health in the US using the Health and Retirement Study survey. We use the nonparametric fuzzy regression discontinuity design to avoid assuming any functional form on the age-health profile and minimize potential bias in identifying the causal effect of retirement on health status in the short term. Estimates indicate an 8% decline in the cognitive functioning score of retirees and a 28% increase in the CESD depression scale. The likelihood of being in good health status declined by 16%. The transition from working to retirement has more significant negative impacts on males than females. In addition, retirement has more considerable adverse effects on less-educated individuals compared to high-educated individuals. The short-term effects of retirement on health are consistent and robust across different bandwidths, weighting kernel functions, and age-profile specifications. Moreover, the Treatment Effect Derivative test results highly support the external validity of the nonparametric estimates of the retirement effect on health.

Has new rural pension system reduced the intake of junk food among rural older adults? Evidence from China.

At present, China has become one of the fastest growing countries in terms of junk food consumption. However, there has been less previous evidence for the effect of endowment insurance on dietary health. Using the data China Family Panel Studies (CFPS) from 2014, this paper exploits a policy, the New Rural Pension System (NRPS), that only the older adults who have reached 60 years old can receive pensions and conduct a fuzzy regression discontinuity (FRD) to address endogeneity and examine the causal effect of the NRPS on the intake of junk food among rural older adults in China. We find that the NRPS can significantly reduce junk food intake among them, which remains robust after a series of robustness tests. In addition, heterogeneity analysis shows that the female, low-educated, unemployed, and low-income groups are more sensitive to the pension shock from the NRPS. The result of our study provides insights to effectively improve people's dietary quality and related policy formulation.

Developing explicit customer preference models using fuzzy regression with nonlinear structure.

In online sales platforms, product design attributes influence consumer preferences, and consumer preferences also have a significant impact on future product design optimization and iteration. Online review data are the most intuitive feedback from consumers on products. Using the value of online review information to explore consumer preferences is the key to optimize the products, improve consumer satisfaction and meet consumer requirements. Therefore, the study of consumer preferences based on online reviews is of great importance. However, in previous research on consumer preferences based on online reviews, few studies have modeled consumer preferences. The models often suffer from the nonlinear structure and the fuzzy coefficients, making it challenging to build explicit models. Therefore, this study adopts a fuzzy regression approach with a nonlinear structure to model consumer preferences based on online reviews to provide reference and insight for subsequent studies. First, smartwatches were selected as the research object, and the sentiment scores of product reviews under different topics were obtained by text mining on the product online data. Second, a polynomial structure between product attributes and consumer preferences was generated to investigate the association between them further. Afterward, based on the existing polynomial structure, the fuzzy coefficients of each item in the structure were determined by the fuzzy regression approach. Finally, the mean relative error and mean systematic confidence of the fuzzy regression with nonlinear structure method were numerically calculated and compared with fuzzy least squares regression, fuzzy regression, adaptive neuro fuzzy inference system (ANFIS) and K-means-based ANFIS, and it was found that the proposed method was relatively more effective in modeling consumer preferences.

Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization.

Regardless of their nature of stochasticity and uncertain nature, wind and solar resources are the most abundant energy resources used in the development of microgrid systems. In microgrid systems and distribution networks, the uncertain nature of both solar and wind resources results in power quality and system stability issues. The randomization behavior of solar and wind energy resources is controlled through the precise development of a power prediction model. Fuzzy-based solar PV and wind prediction models may more efficiently manage this randomness and uncertain character. However, this method has several drawbacks, it has limited performance when the volumes of wind and solar resources historical data are huge in size and it has also many membership functions of the fuzzy input and output variables as well as multiple fuzzy rules available. The hybrid Fuzzy-PSO intelligent prediction approach improves the fuzzy system's limitations and hence increases the prediction model's performance. The Fuzzy-PSO hybrid forecast model is developed using MATLAB programming of the particle swarm optimization (PSO) algorithm with the help of the global optimization toolbox. In this paper, an error correction factor (ECF) is considered a new fuzzy input variable. It depends on the validation and forecasted data values of both wind and solar prediction models to improve the accuracy of the prediction model. The impact of ECF is observed in fuzzy, Fuzzy-PSO, and Fuzzy-GA wind and solar PV power forecasting models. The hybrid Fuzzy-PSO prediction model of wind and solar power generation has a high degree of accuracy compared to the Fuzzy and Fuzzy-GA forecasting models. The rest of this paper is organized as: Section II is about the analysis of solar and wind resources row data. The Fuzzy-PSO prediction model problem formulation is covered in Section III. Section IV, is about the results and discussion of the study. Section V contains the conclusion. The references and abbreviations are presented at the end of the paper.

Understanding the effect of retirement on health behaviors in China: Causality, heterogeneity and time-varying effect.

Retirement is an important turning point during the course of life, but few studies have examined the effects of retirement on a broad range of health behaviors in China. We use the longitudinal data of the China Health and Nutrition Survey (CHNS) from 2004 to 2015 to conduct empirical analysis. Fuzzy discontinuity regression was used to assess the association between retirement and health behaviors in the entire sample and subgroups based on gender and education. A time-varying effect model was used to measure the anticipatory effect, immediate effect and lag effect of retirement. We observed that the transition to retirement was associated with healthier lifestyle habits, such as reduced smoking and alcohol consumption and increased exercise motivation. However, the transition was associated with worse sedentary behavior. No significant statistical association was found between retirement and sleep duration. Men and those with higher education levels are more likely to experience the impact of retirement. The anticipatory effect suggests that as the statutory pension age is predictable, workers adjust their behaviors 4 and 5 years before retirement. The lagged effect indicates that it takes time to develop new habits; thus, retirees change their behaviors 2-3 years after retirement. The paper discusses possible reasons for our findings and proposes several policy implications from the perspectives of the government and society to facilitate the realization of healthy aging.

The impact of retirement on inpatient healthcare utilization in Guangzhou, China: a regression discontinuity analysis of 189,031 health insurance claims.

BACKGROUND: Previous studies suggest that retirement, a major life event, affects overall healthcare utilization. We examine, the effects of retirement on inpatient healthcare utilization, including effect heterogeneity by gender, disease category, and type of health service. METHODS: We used routine health insurance claims data (N = 87,087) spanning the period 2021 - September 2013 from the Urban Employee Basic Medical Insurance (UEBMI), a mandatory social health insurance for working and retired employees in urban China. We applied a non-parametric fuzzy regression discontinuity design using the statutory retirement age in urban China as an exogenous instrument to measure the causal effect of retirement on six measures of inpatient healthcare utilization. RESULTS: Retirement reduced total hospital costs (-84.71 Chinese Yuan (CNY), 95% confidence interval (CI) -172.03 - 2.61), shortened length of hospital stays (-44.59, 95% CI -70.50 - -18.68), and increased hospital readmissions (0.06, 95% CI 0.00 - 0.12) and primary hospital visits (0.06, 95% CI 0.02 - 0.09) among women. Retirement did not significantly change inpatient healthcare utilization among men. The retirement effects among women varied by disease category. Specifically, retirement substantially increased hospitalizations for non-communicable diseases (NCDs), yet had only modest or no effect on hospitalizations for communicable diseases or injuries. Retirement effects among women also varied by the type of services. For relatively inexpensive services, such as nonoperative treatment, there were surges in the extensive margin (hospital readmission). For relatively expensive and invasive services, such as surgeries, retirement reduced the intensive margin (out-of-pocket expenditures and length of stay). CONCLUSIONS: Retirement decreases overall use of inpatient healthcare for women. The examination on the disease-related heterogeneous effects helps with the introduction and implementation of integrated healthcare delivery and appropriate incentive schemes to encourage better use of healthcare resources among older adults.

Self-Perception or Objective State: A Further Study of the Effects of Retirement on Health.

Against the backdrop of an aging global population and the increasing pressure of medical care expenditures for seniors, this paper used a fuzzy regression discontinuity (FRD) model to explore the effects of retirement on the self-assessed health and objective physical and mental health of older people. Using survey data from the China Health and Retirement Longitudinal Study (CHARLS), our model addresses some relevant academic controversies. Our sample was comprised of male respondents from government agencies, enterprises, and public institutions. The research explored the impact of retirement on lifestyle habits and included an in-depth analysis of the mechanism through which retirement influences different aspects of health. The results show that: (1) Retirement does not have any significant impact on objective health, including depression and self-care ability, but it does cause a notable decline in subjective health assessment. (2) Retirement shortened the sleep time of respondents, which may account for lower scores on subjective health self-evaluations, but it did not lead to any noticeable improvement in habits which are harmful to health, such as smoking and drinking. (3) Marriage can help alleviate the problems of depression and smoking among older people, and education has a somewhat broader positive effect on their health and lifestyles; however, neither factor helps to improve the sleep problems of older people. Therefore, this paper recommends that efforts should be made to both optimize retirement policies and seek further ways to improve the health of the retired population.

Introduction to Regression Discontinuity Design.

It is common clinical practice for physicians to refer to specific diagnostic criteria for day-to-day decision-making. In particular, whether or not to provide a particular treatment is often determined by the cutoff value of a relevant diagnostic marker. Regression discontinuity design (RDD) is a method for evaluating scenarios where intervention is determined by the certain cutoff value (e.g., threshold) of a continuous variable. RDD represents a powerful method for assessing intervention effects and outcomes. RDD is underutilized in clinical research and there are many opportunities to apply RDD in this setting. This article introduces the principles of RDD and provides examples of clinical studies that have used this design.

The effect of awarding disability benefits on opioid consumption.

Strong empirical evidence points towards a significantly higher prevalence of opioid consumption among people receiving disability benefits (DB) than in the general population of the United States. However, no previous research established a causal relationship between the decision to award DB to applicants and their subsequent opioid use. We aim to contribute towards filling this gap. There are channels through which awarding DB may both increase and depress opioid consumption, and thus, ex ante, the sign of a potential causal relationship is ambiguous. To correct for the treatment endogeneity, since an individual's age at the time of the decision on an application impacts discontinuously at certain age cutoffs the award decision, we employ a fuzzy Regression Discontinuity model with three age cutoffs used for identification. We find that awarding DB increases the likelihood of using opioids by about 27-30 percentage points. This suggests that the positive association between DB receipt and opioid consumption is likely to be causal.

Comparative assessments of multivariate nonlinear fuzzy regression techniques for egg production curve.

The modelling process of egg production curves, where environmental and genetic factors are highly effective, is quite complex and difficult. In particular, the limitations of measurement and the errors encountered during the measurement process may cause uncertainty in the egg production process. In this study, multivariate nonlinear fuzzy regression analysis was used by configuring neural networks and least squares support vector machines in order to express the uncertainty in the system structure during the egg production process. This method was used to obtain the predicted values for egg production in the fuzzy frame. In the study, two different data sets were used which were measured for egg performance and egg weight variables in daily and weekly time periods. Multivariate nonlinear fuzzy regression analysis results were compared with both the observed values and the multivariate classical regression analysis results. Results of analysis show that multivariate nonlinear fuzzy regression analysis with neural networks is more successful than other methods and can be used as an alternative to classical methods in poultry farming.

The effect of retirement on healthcare utilization: Evidence from China.

We examine the effect of retirement on healthcare utilization in China using longitudinal data. We use a nonparametric fuzzy regression discontinuity design, exploiting the statutory retirement age in urban China as a source of exogenous variation in retirement. In contrast to previous results for developed countries, we find that in China retirement increases healthcare utilization. This increase can be attributed to deteriorating health and in particular to the reduced opportunity cost of time after retirement. For the sample as a whole, income is not a dominating mechanism. People with low education, however, are more likely to forego recommended inpatient care after retirement.

Your retirement and my health behavior: Evidence on retirement externalities from a fuzzy regression discontinuity design.

This paper presents evidence on intra-household retirement externalities by assessing the causal effect of spousal retirement on various health behaviors and health status across 19 European countries. We identify partner's and own retirement effects by applying a fuzzy regression discontinuity design using retirement eligibility as exogenous instruments for spousal and own retirement status. We find significant increases in the frequency and intensity of alcohol consumption combined with a significant decrease in moderate physical activities as a response to partner's retirement. In line with the existing literature, we find that own retirement has significant positive effects on engaging in moderate and vigorous physical activities but also leads to a significant increase in the frequency of alcohol intake. Overall, subjective health is negatively affected by spousal retirement and positively by own retirement.

Hibernation temperature-dependent Pseudogymnoascus destructans infection intensity in Palearctic bats.

White-nose syndrome (WNS) is a fungal disease caused by Pseudogymnoascus destructans that is devastating to Nearctic bat populations but tolerated by Palearctic bats. Temperature is a factor known to be important for fungal growth and bat choice of hibernation. Here we investigated the effect of temperature on the pathogenic fungal growth in the wild across the Palearctic. We modelled body surface temperature of bats with respect to fungal infection intensity and disease severity and were able to relate this to the mean annual surface temperature at the site. Bats that hibernated at lower temperatures had less fungal growth and fewer skin lesions on their wings. Contrary to expectation derived from laboratory P. destructans culture experiments, natural infection intensity peaked between 5 and 6°C and decreased at warmer hibernating temperature. We made predictive maps based on bat species distributions, temperature and infection intensity and disease severity data to determine not only where P. destructans will be found but also where the infection will be invasive to bats across the Palearctic. Together these data highlight the mechanistic model of the interplay between environmental and biological factors, which determine progression in a wildlife disease.

GP responses to price regulation: evidence from a French nationwide reform.

This paper uses a French reform to evaluate the impacts of overbilling restrictions on general practitioner (GP) care provision, fees and incomes. Since 1990, this reform has introduced conditions self-employed GPs must fulfil to be permitted to bill freely. We exploit 2005 and 2008 public health insurance administrative data on GP activity and fees. We use fuzzy regression discontinuity techniques to estimate local causal impacts for GPs who established practices in 1990 and who were constrained by the new regulation to charge regulated prices (compliers). We find that those GPs practices to income effects. In the regulated fee regime, GPs face prices lower by 42% and provide 50% more care than they would do in the unregulated fee regime. Male care provision increasing reaction is larger than the female one, which results in a higher male labour income in the regulated fee regime than with unregulated fees, whereas it is the opposite for women. With regulated fees, GPs limit side-salaried activities, use more lump-sum payment schemes and occupy more often gatekeeper positions.