Background: Infertility affected 10% to 25% of couples globally, and about half of the infertility cases were reported in sub-Saharan Africa. Infertility poses significant social, cultural, and health challenges, particularly for women who often face stigmatization. However, comprehensive and nationally representative data, including prevalence, temporal trends, and risk factors, are lacking, prompting a study in Burkina Faso to address the need for informed policies and programs in infertility care and management. Objectives: This study aims to better understand the spatiotemporal trend of infertility prevalence in Burkina Faso. Methodology: This is a retrospective population-based study of women infertility from healthcare facilities in Burkina Faso, during January 2011 to December 2020. We calculated the prevalence rates of infertility and two disparity measures, and examined the spatiotemporal trend of infertility. Results: Over the 10-year period (2011 to 2020), 143,421 infertility cases were recorded in Burkina Faso healthcare facilities, resulting of a mean prevalence rate of 3.61‰ among childbearing age women and 17.87‰ among women who consulted healthcare facilities for reproductive issues (except contraception). The findings revealed a significant increase of infertility, with the prevalence rate varied from 2.75‰ in 2011 to 4.62‰ in 2020 among childbearing age women and from 13.38‰ in 2011 to 26.28‰ in 2020 among women who consulted healthcare facilities for reproductive issues, corresponding to an estimate annual percentage change of 8.31% and 9.80% respectively. There were significant temporal and geographic variations in the prevalence of infertility. While relative geographic disparity decreased, absolute geographic disparity showed an increasing trend over time. Conclusion: The study highlights an increasing trend of infertility prevalence and significant geographic variation in Burkina Faso, underscoring the urgent necessity for etiologic research on risk factors, psychosocial implica
The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the uncertainty is captured with a given discrete probability distribution over the groups. Such situations arise, for example, in the use of Bayesian imputation methods to assess race and ethnicity disparities with certain insurance, health, and financial data. A widely used method to implement this assessment is the Bayesian Improved Surname Geocoding (BISG) method which assigns a discrete probability over six race/ethnicity groups to an individual given the individual’s surname and address location. Using a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the group means, the probability of a disparity hypothesis is estimated. Four methods are developed and compared with an illustrative data set. Three of these methods are implemented in an R-code and one method in WinBUGS. These methods are programed for any number of groups between two and six inclusive. All the codes are provided in the appendices.
The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer.This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme.A binocular stereo vision sensor composed of two cameras and a light deterction and ranging(LiDAR)sensor is used to jointly perceive the environment,and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map.This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors.Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.
SUN GuoliangPEI ShanshanLONG QianZHENG SifaYANG Rui
