Objective:To identify factors that determine the uptake of in-vitro fertilization(IVF),and to determine the predicting effect of the identified factors on the uptake of IVF among couples attending fertility clinic at ...Objective:To identify factors that determine the uptake of in-vitro fertilization(IVF),and to determine the predicting effect of the identified factors on the uptake of IVF among couples attending fertility clinic at a tertiary health institution in Benin City,Nigeria.Methods:This study adopted a cross-sectional descriptive design.A self-structured questionnaire tested was validated and administered to 250 couples who attended fertility clinic.Data were collected from March 2021 to June 2021 and were analyzed using the Statistical Package for Social Science(SPSS)version 21.Data were presented and interpreted using descriptive statistics and inferential statistics such as Chi-square,univariate statistics and multivariate logistic regression analysis.Results:Among 250 couples,154(61.6%)were willing to adopt IVF.The multivariate logistic regression analysis showed that catholic religion[odds ratio(OR)0.21,95%confident interval(CI)0.54-0.73],family income(OR 1.50,95%CI 1.10-2.00)and age(OR 1.04,95%CI 1.01-1.10)were the major factors that determined the uptake of IVF,with P-value of<0.001,0.018 and 0.031,respectively.Conclusions:The uptake of IVF could be sustained or improved on,if the government should improve on the cost of living for families,and religious leaders need to be encouraged to pass on the information about IVF to their members to be well guided about the possibilities of IVF.展开更多
Background:Chronic obstructive pulmonary disease(COPD)is a common public health problem worldwide.Recent studies have reported that socioeconomic status(SES)is related to the incidence of COPD.This study aimed to inve...Background:Chronic obstructive pulmonary disease(COPD)is a common public health problem worldwide.Recent studies have reported that socioeconomic status(SES)is related to the incidence of COPD.This study aimed to investigate the association between SES and COPD among adults in Jiangsu province,China,and to determine the possible direct and indirect effects of SES on the morbidity of COPD.Methods:A cross-sectional study was conducted among adults aged 40 years and above between May and December of 2015 in Jiangsu province,China.Participants were selected using a multistage sampling approach.COPD,the outcome variable,was diagnosed by physicians based on spirometry,respiratory symptoms,and risk factors.Education,occupation,and monthly family average income(FAI)were used to separately indicate SES as the explanatory variable.Mixed-effects logistic regression models were introduced to calculate odds ratios(ORs)and 95%confidence intervals(CIs)for examining the SES-COPD relationship.A pathway analysis was conducted to further explore the pulmonary function impairment of patients with different SES.Results:The mean age of the 2421 participants was 56.63±9.62 years.The prevalence of COPD was 11.8%(95%CI:10.5%–13.1%)among the overall sample population.After adjustment for age,gender,residence,outdoor and indoor air pollution,body weight status,cigarette smoking,and potential study area-level clustering effects,educational attainment was negatively associated with COPD prevalence in men;white collars were at lower risk(OR:0.60,95%CI:0.43–0.83)of experiencing COPD than blue collars;compared with those within the lower FAI subgroup,participants in the upper(OR:0.68,95%CI:0.49–0.97)tertiles were less likely to experience COPD.Such negative associations between all these three SES indicators and COPD were significant among men only.Education,FAI,and occupation had direct or indirect effects on pulmonary function including post-bronchodilator forced expiratory volume in 1 s/forced vital capacity(FEV1/FVC),FEV1,FVC,and FEV1 percentage of predicted.Education,FAI,and occupation had indirect effects on pulmonary function indices of all participants mainly through smoking status,indoor air pollution,and outdoor air pollution.We also found that occupation could affect post-bronchodilator FEV1/FVC through body mass index.Conclusions:Education,occupation,and FAI had an adverse relationship with COPD prevalence in Jiangsu province,China.SES has both direct and indirect associations with pulmonary function impairment.SES is of great significance for COPD morbidity.It is important that population-based COPD prevention strategies should be tailored for people with different SES.展开更多
Inferring people’s Socioeconomic Attributes(SEAs),including income,occupation,and education level,is an important problem for both social sciences and many networked applications like targeted advertising and persona...Inferring people’s Socioeconomic Attributes(SEAs),including income,occupation,and education level,is an important problem for both social sciences and many networked applications like targeted advertising and personalized recommendation.Previous works mainly focus on estimating SEAs from peoples’cyberspace behaviors and relationships,such as the content of tweets or the social networks between online users.Besides cyberspace data,alternative data sources about users’physical behavior,like their home location,may offer new insights.More specifically,in this paper,we study how to predict a person’s income level,family income level,occupation type,and education level from his/her home location.As a case study,we collect people’s home locations and socioeconomic attributes through a survey involving 9 provinces and 85 cities in China.We further enrich home location with the knowledge from real estate websites,government statistics websites,online map services,etc.To learn a shared representation from input features as well as attribute-specific representations for different SEAs,we propose H2SEA,a factorization machine-based multi-task learning method with attention mechanism.Extensive experiment results show that:(1)Home location can clearly improve the estimation accuracy for all SEA prediction tasks(e.g.,80.2%improvement in terms of F1-score in estimating personal income level);(2)The proposed H2SEA model outperforms alternative models for SEA inference in terms of various evaluation metrics,such as Area Under Curve(AUC),F-measure,and specificity;(3)The performance of specific SEA prediction tasks(e.g.,personal income)can be further improved if H2SEA only focuses on cities or villages due to urban-rural gap in China;(4)Compared with online crawled housing price data,the area-level average income and Points Of Interest(POI)are more important features for SEA inferences in China.展开更多
文摘Objective:To identify factors that determine the uptake of in-vitro fertilization(IVF),and to determine the predicting effect of the identified factors on the uptake of IVF among couples attending fertility clinic at a tertiary health institution in Benin City,Nigeria.Methods:This study adopted a cross-sectional descriptive design.A self-structured questionnaire tested was validated and administered to 250 couples who attended fertility clinic.Data were collected from March 2021 to June 2021 and were analyzed using the Statistical Package for Social Science(SPSS)version 21.Data were presented and interpreted using descriptive statistics and inferential statistics such as Chi-square,univariate statistics and multivariate logistic regression analysis.Results:Among 250 couples,154(61.6%)were willing to adopt IVF.The multivariate logistic regression analysis showed that catholic religion[odds ratio(OR)0.21,95%confident interval(CI)0.54-0.73],family income(OR 1.50,95%CI 1.10-2.00)and age(OR 1.04,95%CI 1.01-1.10)were the major factors that determined the uptake of IVF,with P-value of<0.001,0.018 and 0.031,respectively.Conclusions:The uptake of IVF could be sustained or improved on,if the government should improve on the cost of living for families,and religious leaders need to be encouraged to pass on the information about IVF to their members to be well guided about the possibilities of IVF.
基金supported by grants from the National Key Research and Development Program of China(No.2018YFC1313602 and 2016YFC1302603)National Natural Science Foundation of China(No.81820108001,81670029,and 81470273)+8 种基金Jiangsu Jian-kang Vocational College Project(No.JKC202012)Science and Technology Development Fund of Nanjing Medical University(No.NMUB2020190)Nanjing Medical Science and Technique Development Foundation(No.QRX17199)Nanjing Medical Science and Technique Development Foundation(No.QRX11038)National China Medicine Science and Technology Special Project of Jiangsu Province(No.BL2014083)Six Talent Peak Project of Jiangsu Province(No.2012-WS-l 14)Nanjing Science and Technology Plan Project(No.201803064)Jiangsu Pharmaceutical Association Project(No.Q2018049)Nanjing Key Project of Science and Technology(No.2019060002).
文摘Background:Chronic obstructive pulmonary disease(COPD)is a common public health problem worldwide.Recent studies have reported that socioeconomic status(SES)is related to the incidence of COPD.This study aimed to investigate the association between SES and COPD among adults in Jiangsu province,China,and to determine the possible direct and indirect effects of SES on the morbidity of COPD.Methods:A cross-sectional study was conducted among adults aged 40 years and above between May and December of 2015 in Jiangsu province,China.Participants were selected using a multistage sampling approach.COPD,the outcome variable,was diagnosed by physicians based on spirometry,respiratory symptoms,and risk factors.Education,occupation,and monthly family average income(FAI)were used to separately indicate SES as the explanatory variable.Mixed-effects logistic regression models were introduced to calculate odds ratios(ORs)and 95%confidence intervals(CIs)for examining the SES-COPD relationship.A pathway analysis was conducted to further explore the pulmonary function impairment of patients with different SES.Results:The mean age of the 2421 participants was 56.63±9.62 years.The prevalence of COPD was 11.8%(95%CI:10.5%–13.1%)among the overall sample population.After adjustment for age,gender,residence,outdoor and indoor air pollution,body weight status,cigarette smoking,and potential study area-level clustering effects,educational attainment was negatively associated with COPD prevalence in men;white collars were at lower risk(OR:0.60,95%CI:0.43–0.83)of experiencing COPD than blue collars;compared with those within the lower FAI subgroup,participants in the upper(OR:0.68,95%CI:0.49–0.97)tertiles were less likely to experience COPD.Such negative associations between all these three SES indicators and COPD were significant among men only.Education,FAI,and occupation had direct or indirect effects on pulmonary function including post-bronchodilator forced expiratory volume in 1 s/forced vital capacity(FEV1/FVC),FEV1,FVC,and FEV1 percentage of predicted.Education,FAI,and occupation had indirect effects on pulmonary function indices of all participants mainly through smoking status,indoor air pollution,and outdoor air pollution.We also found that occupation could affect post-bronchodilator FEV1/FVC through body mass index.Conclusions:Education,occupation,and FAI had an adverse relationship with COPD prevalence in Jiangsu province,China.SES has both direct and indirect associations with pulmonary function impairment.SES is of great significance for COPD morbidity.It is important that population-based COPD prevention strategies should be tailored for people with different SES.
基金The research work was partly funded by the European Union’s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie(No.824019)the Tsinghua-Gottingen Student Exchange Project(No.IDSSSP-2017001).
文摘Inferring people’s Socioeconomic Attributes(SEAs),including income,occupation,and education level,is an important problem for both social sciences and many networked applications like targeted advertising and personalized recommendation.Previous works mainly focus on estimating SEAs from peoples’cyberspace behaviors and relationships,such as the content of tweets or the social networks between online users.Besides cyberspace data,alternative data sources about users’physical behavior,like their home location,may offer new insights.More specifically,in this paper,we study how to predict a person’s income level,family income level,occupation type,and education level from his/her home location.As a case study,we collect people’s home locations and socioeconomic attributes through a survey involving 9 provinces and 85 cities in China.We further enrich home location with the knowledge from real estate websites,government statistics websites,online map services,etc.To learn a shared representation from input features as well as attribute-specific representations for different SEAs,we propose H2SEA,a factorization machine-based multi-task learning method with attention mechanism.Extensive experiment results show that:(1)Home location can clearly improve the estimation accuracy for all SEA prediction tasks(e.g.,80.2%improvement in terms of F1-score in estimating personal income level);(2)The proposed H2SEA model outperforms alternative models for SEA inference in terms of various evaluation metrics,such as Area Under Curve(AUC),F-measure,and specificity;(3)The performance of specific SEA prediction tasks(e.g.,personal income)can be further improved if H2SEA only focuses on cities or villages due to urban-rural gap in China;(4)Compared with online crawled housing price data,the area-level average income and Points Of Interest(POI)are more important features for SEA inferences in China.