Estimating the failure rate of the log-logistic distribution by smooth adaptive and bias-correction methods

[[abstract]]The Log-logistic distribution has successfully earned attention in practical applications due to its good statistical properties. Because the traditional maximum likelihood estimators of the Log-logistic distribution parameters do not have an explicit form and are biased when the sample size is small. Therefore, the estimation and prediction of the failure rate is not well. In this study, we study the quality of the maximum likelihood, asymptotic maximum likelihood and bias-corrected maximum likelihood methods, and propose a smooth adaptive estimation method for estimating the Log-logistic distribution parameters. To reduce the bias of the asymptotic maximum likelihood and smooth adaptive estimators of the Log-logistic distribution parameters, the bias-corrected method is used to improve the asymptotic maximum likelihood and smooth adaptive estimation methods. Two new bias-corrected estimation methods are also proposed to obtain reliable estimates of the Log-logistic distribution parameters. An intensive Monte Carlo simulation study is conducted to evaluate the performance of these estimation methods. Simulation results show that the smooth adaptive and two new bias-corrected estimation methods are more competitive than other competitors. Finally, two real example is used for illustrating the applications of the smooth adaptive, CAML and CSA estimation methods.[[notice]]補正完

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Deep learning‐enabled imaging flow cytometry for high‐speed Cryptosporidium and Giardia detection

International audienceImaging flow cytometry has become a popular technology for bioparticle image analysis because of its capability of capturing thousands of images per second. Nevertheless, the vast number of images generated by imaging flow cytometry imposes great challenges for data analysis especially when the species have similar morphologies. In this work, we report a deep learning-enabled high-throughput system for predicting Cryptosporidium and Giardia in drinking water. This system combines imaging flo

Substrate signal inhibition in Raman analysis of microplastic particles

In Raman analysis, the substrate material serves very often for signal enhancement, especially when metallic surfaces are involved; however, in other cases, the substrate has an opposite effect as it is the source of a parasitic signal preventing the observation of the sample material of interest. This is particularly true with the advent of microfluidic devices involving either silicon or polymer surfaces. On the other hand, in a vast majority of Raman experiments, the analysis is made on a horizontal support holding the sample of interest. In our paper, we report that a simple tilting of the supporting substrate, in this case, silicon, can drastically decrease and eventually inhibit the Raman signal of the substrate material, leading to an easier observation of the target analyte of the sample, in this case, microplastic particles. This effect is very pronounced especially when looking for tiny particles. Explanation of this trend is provided thanks to a supporting experiment and further numerical simulations that suggest that the lensing effect of the particles plays an important role. These findings may be useful for Raman analysis of other microscale particles having curved shapes, including biological cells.Published versionThis project received support from the I-SITEFUTURE Initiative (Reference ANR-16-IDEX-0003) in the frame of the project NANO-4-WATER as well as the METAWATER Project (ANR-20-CE08-0023 META-WATER)