Multivariate Regression Estimates Research Articles

Prevalence and predictors of men's involvement in pregnancy care in Modakeke, Southwest Nigeria.

Maternal mortality in developing countries remains a major public health concern and lack of men's support for their spouse during pregnancy contributes to this adverse outcome. This study examined the level and determinants of men's involvement in pregnancy care in Modakeke, Southwest Nigeria. A community-based cross-sectional, mixed-methods study involving quantitative and qualitative data. A multistage sampling strategy was used to select the study participants. The study involved 414 male respondents interviewed using a structured interviewer-administered questionnaire. The interview was complemented with one focus group discussion facilitated using an unstructured interview guide. Quantitative data were analyzed using descriptive and inferential analytical techniques while qualitative data were explored using thematic content analysis. Findings revealed a 55% level of involvement in pregnancy care among the participants. However, involvement rates were higher among those who were younger, married, from monogamous family, with secondary or tertiary education, low-income earners, and holding positive perception about nontraditional gender roles. Multivariate logistic regression estimates indicated significantly lower odds of involvement among unmarried men than the married but increased odds among those who had secondary or higher education relative to the uneducated, and among those whose partners received antenatal care from multiple providers compared to from health facilities only. Furthermore, significantly reduced odds of involvement were associated with holding nonpositive perceptions towards accompanying spouse to antenatal care clinic and being involved in general pregnancy care as opposed to holding positive perception. Perceived challenges undermining male involvement as highlighted during focus group discussion include time constraints due to job demands, prohibitive sociocultural norms, rebuke and unconducive health facility environment. The level of involvement in pregnancy care is suboptimal among the study participants with considerable sociodemographic, socioeconomic and sociocultural dimensions. Enhancing men's involvement in pregnancy care requires community-based awareness-raising interventions that emphasize crosscutting benefits of male partner's participation in pregnancy-related care and address known sociocultural barriers impeding active involvement.

Risk of multiple lower and upper urinary tract problems among male older adults with type-2 diabetes: a population-based study

Aim This study explores the risk of diabetes-associated lower and upper urinary tract diseases among male older adults aged 45 and above in India. Methods Longitudinal Ageing Study in India (LASI), 2017–2018 data was used in this study. The prevalence of various urinary tract problems and diabetes among male older adults was estimated by background characteristics using bivariate cross-tabulation. In addition, multivariate logistic regression was applied to examine the likelihood of urological disorders associated with diabetes. Result The prevalence of incontinence was highest among male older adults with diabetes, followed by kidney stones, benign prostatic hyperplasia, and chronic renal failure. Multivariate logistic regression estimation showed that men diagnosed with diabetes were 80% more likely to experience chronic renal failure, 78% more likely to suffer from incontinence, and 37% more likely to suffer from kidney stones than those without diabetes when controlling for various socio-demographic, behavioral, and co-morbidity status of the older adults. Conclusions The study findings suggest that diabetes is associated with multiple urinary complications among male older adults in India and needs more careful investigation of the phenomenon. Independent risk factors such as changes in lifestyle with regular monitoring and diagnosis may help to prevent the progression of diabetes and reduce the risk of diabetes-associated lower and upper urinary tract diseases among male older adults.

Landrace and GM maize cultivars’ selection choices among rural farming households in the Eastern Cape Province, South Africa

ABSTRACT Maize plays a significant role among rural farming households in South Africa. The study therefore estimated the drivers of maize cultivars’ selection choices among rural farming households focusing on the commonly grown cultivars from the study area (Landrace and genetically modifies [GM] maize). A cross-sectional survey of 650 respondents randomly selected from Port St Johns and King Sabata Dalindyebo Local Municipalities of the Eastern Cape Province of South Africa were used. Descriptive results revealed that a significant number of the respondents from the study area (65%) were growing Landrace maize cultivars, followed by GM maize (31%), improved OPVs (3%) and conventional hybrids (1%). Multivariate Probit regression estimates show that GM maize cultivars’ selection is positively influenced by the amount of rainfall (1% level), household size (5% level), education (1% level), size of arable land (10% level) and access to cell phones (5% level), whilst negatively influenced by the employment status (5%). Conversely, the selection of Landrace maize cultivars is negatively influenced by the of amount rainfall (1% level), education (1% level), income (10% level), access to cell phone (10% level) and radio (10% level) whilst positively influenced by the number of livestock (5% level). The study therefore argues that GM maize cultivars may be meaningfully promoted in high rainfall areas, focusing on arable land sizes and targeted awareness campaigns. The promotion of Landrace maize cultivars may be targeted in low rainfall areas in a mixed farming setting to enhance the complementarity of maize and livestock.

A Multivariate Regression Estimator of Levels and Change for Surveys Over Time

Abstract Rotations are often used for panel surveys, where the observations remain in the sample for a predefined number of periods and then rotate out. The information of previous waves can be exploited to improve current estimates. We propose a multivariate regression estimator which captures all information available from both waves. By adding additional auxiliary variables describing the information of the rotational design, the proposed estimator captures the sample correlation between waves. It can be used for the estimation of levels and changes.

Estimation of ecological footprint based on tourism development indicators using neural networks and multivariate regression.

The ecological footprint has attracted a lot of attention in the top tourism destination countries, and this issue may be worrying. This study aims to estimate the ecological footprint, using such indicators as economic growth, natural resources, human capital, and the number of tourists in top tourism destination countries. For this purpose, artificial neural network models and multivariate regression were used for a period of 24years (1995-2019). The results of the study showed a significant positive correlation between economic growth and ecological footprint. Multivariate regression estimation (R = 0.75) is weaker than neural network models (R = 96.3). Regarding predicting the ecological footprint, neural network models have better performance in comparison with the multivariate regression statistical methods. Accordingly, one can say that for planning ecological footprint, deeper look at neural networks can be more effective in predicting top tourism destination countries.

Prevalence of Obstructive Sleep Apnea Symptoms Among the Adult Population in Al-Ahsa, Saudi Arabia.

Introduction Obstructive sleep apnea (OSA) is a disorder characterized by repeated episodes of partial or complete obstruction of the airway during sleep. OSA can lead to serious long-term complications if left untreated. Aim This study aims to assess the prevalence of OSA symptomsamong the adult population in Al-Ahsa, Saudi Arabia. Subjects and methods This is a cross-sectional study including the adult population living in Al-Ahsa, Saudi Arabia. A self-administered questionnaire was distributed to the targeted population using an online survey. The questionnaire was divided into two sections, where the first part was about the characteristics of the participant (i.e., age, gender, marital status, etc.), and the second part was the Epworth Sleepiness Scale (ESS) questionnaire to evaluate OSA symptoms. Results Three hundred and sixty adult subjects participated in this study by responding to the questionnaire (58.1% males vs. 41.9% females). The respondents' median age was 30 years old. The prevalence of OSA symptoms was 26.9%. In terms of OSAsymptom severity, mild, moderate, and severe excessive daytime sleepiness were found among 12.5%, 8.3%, and 6.1%, respectively. The prevalence ofOSA symptoms was significantly higher in the older group(>30 years; p=0.004), married participants (p=0.008), and obese or overweight (BMI ≥25 kg/m2; p=0.002). Multivariate regression estimates showed that being obese or overweight (BMI ≥25 kg/m2) was the sole independent significant predictor associated with increased odds of OSA symptoms. Conclusion The prevalence of OSA symptoms among Al-Ahsa residents was 26.9% with prevalence being higher in males than females. Further investigations are needed to establish the prevalence of OSAand understand its influence on the adult population in our region. People who were suspected to have OSA in this study should be reassessed using polysomnography to confirm the diagnosis. Patients with suspected OSA should beencouraged to adopt lifestyle modifications specifically targeted to/focused on weight reduction and smoking cessation.

The social determinants of substance use associated with deaths of despair: Individual risks and population impacts

As the incidence of deaths from external causes including poisonings, suicide, and alcohol-related liver disease, increases in countries such as the United States and Canada, a better understanding of the fundamental social determinants of the substance use underlying these so-called “deaths of despair”, at the population level, is needed. Using data from the nationally representative data from the Canadian Community Health Survey (2003, 2015–2016, 2018 cycles) (N = 30,729), the independent associations between age, sex, marital status, immigrant status, race/ethnicity, education, income, rurality, affective health and the use of illicit substances, opioids (without distinction for prescription status), problematic levels of alcohol, and combined past-year use (≥2) of substances, were explored using multivariate logistic regression, marginal risk, and population attributable fraction estimation, with propensity score-adjusted sensitivity analyses. Males, those who were under 29 years, without a partner, born in Canada, White, or had an affective disorder reported both higher use of individual substances and multiple substances in the past year. Social determinants appear to explain a substantial proportion of substance use patterns overall. Between 10% and 45% of illicit substance, problematic alcohol, and polysubstance use prevalence was attributable to non-partnered marital status, non-immigrant status, and White race/ethnicity. Of opioid use prevalence, 25% was attributable to White race/ethnicity, 13% to affective disorder status and 4% to lower-income. Though not all substance use will result in substance-related morbidity or mortality, these findings highlight the role of social determinants in shaping the intermediary behavioural outcomes that shape population-level risk of “deaths of despair”.

Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model

Efficiency mapping has an important place in examining the maximum efficiency distribution as well as the energy consumption of designed electric motors at maximum torque and speed. Performing analysis at all operating points with FEM analysis in the motor design process requires high processing costs and time. In this article, a machine learning-based multivariate polynomial regression estimation model was developed to overcome these costly processes from FEM analysis. With the proposed method, the operating points of the motors in different conditions during the design process can be predicted in advance with high accuracy. In the study, two different models are developed for efficiency map and core loss estimation of interior permanent magnet synchronous motor design. The developed models use few parameters and predict with high accuracy. Estimation models shorten the design process and offer a less complex model. Obtained results are validated by comparison with FEM analysis.

Reproductive Burden and Its Impact on Female Labor Market Outcomes in India: Evidence from Longitudinal Analyses

We use nationally representative data from two waves of the Indian Human Development Survey to examine the role of inter-temporal changes in fertility behavior in influencing female labor market outcomes. Our multivariate regression estimates show that an increase in the number of children reduces labor force participation and earnings. We further investigated the impact of fertility changes on transitions from the labor market. The results show that women who had more than three children in both rounds of the survey had a 3.5% points higher probability of exiting from the labor market than their counterparts with two or fewer children net of other socio-demographic factors. Disaggregated analyses by caste, economic, educational status, and region show that the probability of dropping out of the labor market due to fertility changes varies by region and is greater for non-poor and primary to secondary schooling women and those from socially disadvantaged castes than poor, non-educated, and socially advantageous women.

Robust approaches to redundancy analysis

Redundancy analysis is used to examine the interrelationships between two sets of variables, one set being dependent on the other. It aims to maximize the proportion of variance of the dependent variables that can be explained by each successive uncorrelated linear combination of the explanatory variables. It is an alternative to canonical correlation analysis when there is no symmetry in the variables. In this work, we study two different approaches to robustly estimate redundancy parameters. As a first approach, we consider a plug-in method based on robust correlation matrices capable of achieving simultaneously high efficiency under a Gaussian model and high resistance to outliers. As a second approach, we explore the relationship between redundancy analysis and multivariate linear regression and propose a method based on robust multivariate linear regression estimators. For elliptical distributions, the local robustness of the redundancy analysis based on robust scatter matrices is studied using the influence function. A simulation study shows that robust estimators perform better than the classical estimator and compares the proposals under contaminated samples. The performance of the proposals in a real data example is presented.

Mixed-type multivariate response regression with covariance estimation.

We propose a new method for multivariate response regression and covariance estimation when elements of the response vector are of mixed types, for example some continuous and some discrete. Our method is based on a model which assumes the observable mixed‐type response vector is connected to a latent multivariate normal response linear regression through a link function. We explore the properties of this model and show its parameters are identifiable under reasonable conditions. We impose no parametric restrictions on the covariance of the latent normal other than positive definiteness, thereby avoiding assumptions about unobservable variables which can be difficult to verify in practice. To accommodate this generality, we propose a novel algorithm for approximate maximum likelihood estimation that works “off‐the‐shelf” with many different combinations of response types, and which scales well in the dimension of the response vector. Our method typically gives better predictions and parameter estimates than fitting separate models for the different response types and allows for approximate likelihood ratio testing of relevant hypotheses such as independence of responses. The usefulness of the proposed method is illustrated in simulations; and one biomedical and one genomic data example.

Representative Bureaucracy and Organizational Justice in Mediation.

Studies of representative bureaucracy (RB) argue public organizations reflective of the public they serve exhibit better outcomes, especially when serving underrepresented groups. RB theory attributes improved outcomes either to the actions representative bureaucrats take (active representation), or a greater perception of trust and legitimacy toward them by service recipients (symbolic representation), largely treating active and symbolic representation as separate phenomena. We explore the intricate relationship between bureaucracies and the populations they serve by observing the cross-influence between active and symbolic representation, as revealed by self-reported outcomes in discrimination complaints (N = 1,372) referred for voluntary mediation in the United States Postal Service, the REDRESS© program, a context in which mediators are highly limited in representing a claimant's interests given the requirement of impartiality. In exit surveys measuring employee perceptions of organizational justice, we observed the impact of race and gender representation by gauging changes in reported satisfaction when a mediator's race or gender matched the nature of the complaint in cases of race or sex discrimination and sexual harassment, via multivariate regression estimation. These analyses support RB theory regarding sexual harassment complaints, where complainants rated outcomes significantly more favorably for female mediators. We found a negative correlation between female mediators and sex discrimination complaints, as well as African American mediators and race discrimination complainants. To explain this discrepancy, we argue that interactions between symbolic and active representation determine the expectations and perceptions placed on bureaucrats. When a bureaucrat does not meet those expectations, service recipients tend to have a more negative view of organizational justice outcomes.

Reproductive burden and its impact on female labor market outcomes in India: Evidence from longitudinal analyses

We use nationally representative data from two waves of the Indian Human Development Survey to provide causal evidence on the role of inter-temporal changes in fertility behaviour in influencing female labor market outcomes. Our multivariate regression estimates show that an increase in the number of children reduces labor force participation and earnings. We further investigated the impact of fertility changes on transitions from the labor market. The results show that women who had more than three children in both rounds of the survey had a 3.5 percentage points higher probability of exiting from the labor market. Disaggregated analyses by caste, economic status and region show regional heterogeneity, and the probability of dropping-out of the labor market due to fertility changes is greater for non-poor women and those from socially disadvantaged castes.

Neighborhood area-level social determinants and receipt of mammography screening in an area with high deprivation: A retrospective cohort study.

e18550 Background: Early detection through screening mammography has been shown to decrease breast cancer mortality. Screening mammography rates remains low among racial/ethnic minorities and patients with socioeconomic deprivation (SED). Most studies evaluating the role of area-level social determinants of health and breast cancer screening have included only a small number of variables; in this study, a comprehensive and granular measure of socioeconomic deprivation (SED) which included 17 variables was used to determine an association with screening mammogram completion. Methods: A retrospective cohort study was conducted at an academic hospital system between 2014-2020 to identify asymptomatic female patients who received screening mammogram referrals in their primary care clinic after they were deemed eligible per screening guidelines. Patients were assessed for mammogram completion at their annual visits. SED was evaluated using the area deprivation index (ADI), a measure of 17 variables including education, housing, and income at the census block group level. Other covariates analyzed were insurance status, age, and race. Chi-square test, Kruskal-Wallis test and a multivariate logistic regression model were used for statistical analysis. Results: 856 women were referred for screening mammography. 324 (38%) underwent mammogram. Patients with high, moderate, and low SED comprised 69 (8%), 287 (34%) and 500 (58%) of the cohort, respectively. In multivariable analysis, SED and race were not associated with higher screening rates. Uninsured and self-pay patients had the lowest odds of screening mammography completion (AOR 0.22; 95% 0.08, 0.60) and Medicare patients had decreased odds of mammogram completion relative to privately insured patients (AOR 0.64; 95% CI 0.43, 0.97). Older age was associated with a slightly higher odds of mammography completion (AOR 1.02; 95% CI 1.00, 1.04). Conclusions: The receipt of screening mammography was low among all patients relative to previously published rates. Uninsured/self-pay status was the strongest indicator for completion of mammography. Additional research is needed to understand the barriers that may influence mammography completion in this population with high socioeconomic deprivation. Multivariate Logistic Regression Estimates for Associations Between Mammogram Completion and SED category.[Table: see text]

ECONOMIC SHOCKS ON PARTNERS’ TRADE; AFRICA AND ASEAN ON CHINA

This research studies the impact of macroeconomic shocks from African and the Association of Southeast Asian Nations (ASEAN) on China’s bilateral trade with them. Data on (GDP) per capita, FDI, inflation, unemployment rates, and trade openness (TO) of China’s African and ASEAN partners were sourced from the World Bank whilst imports and exports data were from the world integrated trade solutions (WITS). It uses the gravity model as a basis and the panel corrected standard errors (PCSE) as well as multivariate regression estimators. The findings reveal that per capita of China’s partners have a strong positive impact on trade with them. Trade openness is reported to increase China’s imports but reduce exports to these partners. Further, an increase in FDI inflows to China’s trade partners leads to an increase in both imports and exports of China. KEYWORDS: Economic shocks, international trade, China, Africa, ASEAN, gravity model

Inequality in Access to COVID-19 Vaccines: Evidence from the Household Heads and Household Helps from Dhaka City

Inequality in vaccination has been perceived as a research problem in the recent COVID-19 pandemic. So far, this type of inequality has been investigated in the particular context of race, ethnicity, gender, regions in different parts of the world, with no study conducted so far in the context of Bangladesh, an important South Asian country. We have conducted an online purposive random sampling of 311 household heads in April 2021 from Dhaka, Bangladesh, with their respective domestic or household helps to examine the vaccination status across socioeconomic status, age, income, access to the internet, cellphone, laptop, or other computer devices, national identity card. Using univariate descriptive statistics, bivariate contingency table analysis, and multivariate logistic regression estimation, we find that in Dhaka, the largest megacity and the capital of Bangladesh, domestic or household workers above 40 years of age have mostly not taken COVID-19 vaccination at 5% level of significance. At the same time, their associated household heads are mostly vaccinated with two doses if they are above 40. Being a household head, access to cell phones, internet, and computer facilities are significant determinants to online registration and consequent COVID-19 vaccination. The results have posed a question regarding the efficacy of the ongoing vaccination drive and the observed inequality or vaccine gap, primarily driven by class differences, whereas the government policy towards vaccination was intended to be universal across classes.

On the Estimation of Variance of Calibration Regression Estimators with Multiple Auxiliary Information

This paper introduces the concept of calibration estimators to Statistical Regression Estimation and proposes a multivariate calibration regression (M-REG) estimator of population mean in stratified random sampling. It develops a new approach to variance estimation that is more efficient in estimating populations with multiple auxiliary variables using the principle of analysis of variance (ANOVA). The relative performance of the new variance estimation method with respect to the estimation of variance of the proposed M-REG estimator is compared empirically with a corresponding global variance estimation method. Analysis and evaluation presented, proved the dominance of the suggested new approach to variance estimation.

ANALYSIS OF THE BRI AND CHINA’S OFDI IN SUB-SAHARAN AFRICA

The “Belt and Road Initiative” (BRI) has been launched by the Chinese government in 2013. The aim was to stimulate cross-border economic development in massive geographical areas covering Asia, Oceania, Europe, Africa, and Latin America which accounts for 80% and 40% of the world population and gross domestic product (GDP), respectively. The BRI has devised an extension of the “going global” strategy to reconfigure China’s overseas sector in order to extend its spillovers, and create more development opportunities for participating countries. In practice, cross-border infrastructure was a comprehensive role to reduce transportation cost; however, the BRI was vast by nature that includes financial support, policy cooperation, investment, trade facilitation, and people-to-people exchanges for the humanitarian strategy. Against this backdrop, the overarching objective of this study was to analyze the impact of the BRI and Chinese outward foreign direct investment (OFDI) on the bilateral trade between China and Sub-Saharan Africa countries. The investigation was carried out using a trade gravity model, balanced panel dataset, and multivariate regression estimation strategy for robustness checks covering 16 years. The result showed that Chinese OFDI, home, and host country’s GDP and GDP per capita income variables have a positive and statistically significant impact on the bilateral trade. Moreover, the BRI has explained positively on the bilateral trade; however, it does not have enough evidence to stimulate significantly, and it usually takes a long time for the effects of the BRI investment on trade and OFDI. The study also found that geographical distance and official exchange rates have explained negatively and statistically significant impact on the bilateral trade.

VOLUNTEER ACTIVITY CHANGES AFTER MIDDLE YEARS OLD AND THE INFLUENCES OF VOLUNTEERING ON WELL-BEING

The objectives of this paper are to examine volunteer activity changes over the life courses and the long-term influences of volunteer activity on the well-being. The paper analyzes the KLoSA data from wave 1 to wave 5, and selects 10,254 persons over 45 years old as study samples in the base year. For the statistical analysis, the two-way connected line plot and transition probability analysis techniques are employed and the GEE method is used for the multivariate regression estimation of the coefficients. The results show that the volunteer activities increase highest in one’s late 40s and decrease abruptly from the age, and slow down 50s to one’s late 70s, showing a repeating rise and fall pattern. Entering one’s 80s, the volunteer activities come to be stable relatively than the previous years. And, the probability of continuing the volunteer status after 2 years is about 31.2 percent and the probability of stopping volunteer activity comes to be around 68.9 percent. As a last, the regression analysis results show that there is a positive relationship between volunteerism and well-being variables. Expecially the interaction term is statistically significant, showing a negative sign of the coefficient. This implies that the volunteerism contributes to increasing the well-being but decreasing by engaging excessively. The results support the previous study outcomes that excessive volunteer activities are not good for the health.

Navigating the early career: The social stratification of young workers’ employment trajectories in Italy

This paper contributes to the debate on early-career precariousness and inequalities against the backdrop of labour market deregulation. The analyses focus on a cohort of young Italian workers who entered the labour market after the deregulatory reforms of the late 1990s and early 2000s. Instead of looking at the timing of isolated transitions between employment states, 7-year-long trajectories are considered to identify differences by gender and education in the probability of experiencing more stable and protected early-careers. Italy is a particularly interesting country-case because of its high youth unemployment rates and difficult school-to-work transitions into the protected labour market segment. The analyses draw on AD-SILC, a unique data source that combines administrative records of complete employment histories and survey data and offers an unprecedented, highly detailed distinction between a wide range of non-standard employment relationships. Applying sequence analysis, eight early-career types characterized by different degrees of employment continuity and stability are identified. Multivariate logistic regression estimates show that women—especially if lower-educated—are more likely than men to experience pathways characterized by instability and weak (or absent) employment and social security protection, that ultimately lead to more precarious early careers.