Uptake of in-vitro fertilization among couples attending fertility clinic in a tertiary health institution

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.

Estimating Multiple Socioeconomic Attributes via Home Location-A Case Study in China

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.

Association between socioeconomic status and chronic obstructive pulmonary disease in Jiangsu province, China: a population-based study

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.