The COVID-19 pandemic has significantly altered employment and income distribution, impacting women and men differently. This study investigates the negative effects of COVID-19 on the labour market, focusing on the gender gap in five countries in the Middle East and North Africa (MENA) region. The study indicates whether women are more susceptible to losing their jobs, either temporarily or permanently, switching their primary occupation, and experiencing decreased working hours and income compared to men during the COVID-19 outbreak. The study utilizes a multivariate Probit model to estimate the relationship between gender and adverse labour outcomes controlling for correlations among outcomes. Data are obtained from the Combined COVID-19 MENA Monitor Household Survey, covering Egypt, Tunisia, Morocco, Jordan, and Sudan. The findings of this study offer empirical evidence of the gender gap in labour market outcomes during the pandemic. Women are more likely than men to experience negative work outcomes, such as permanent job loss and change in their main job. The increased childcare and housework responsibilities have significantly impacted women's labour market outcomes during the pandemic. However, the availability of telework has reduced the likelihood of job loss among women. The study's results contribute to a better understanding of the impact of COVID-19 on gender inequality in understudied MENA countries. Mitigation policies should focus on supporting vulnerable women who have experienced disproportionate negative effects of COVID-19.

The Inverse Gaussian Regression Model (IGRM) is used when the response variable is positively skewed and follows the inverse Gaussian distribution. In this article, we propose a Liu-type estimator to combat multicollinearity in the IGRM. The variance of the Maximum Likelihood Estimator (MLE) is overstated due to the presence of severe multicollinearity. Moreover, some estimation methods are suggested to estimate the optimal value of the shrinkage parameter. The performance of the proposed estimator is compared with the MLE and some other existing estimators in the sense of mean squared error through Monte Carlo simulation and different real-life applications. Under certain conditions, it is concluded that the proposed estimator is superior to the MLE, ridge, and Liu estimator.

In the logistic regression model, the variance of the maximum likelihood estimator is inflated and unstable when the multicollinearity exists in the data. There are several methods available in literature to overcome this problem. We propose a new stochastic restricted biased estimator. We study the statistical properties of the proposed estimator and compare its performance with some existing estimators in the sense of scalar mean squared criterion. An example and a simulation study are provided to illustrate the performance of the proposed estimator. 

Due to the large amount of accidents negatively affecting the wellbeing of the survivors and their families, a substantial amount of research is conducted to determine the causes of road accidents. This type of data come in the form of non-negative integers and may be modelled using the Poisson regression model. Unfortunately, the commonly used maximum likelihood estimator is unstable when the explanatory variables of the Poisson regression model are highly correlated. Therefore, this paper proposes a new almost unbiased estimator which reduces the instability of the maximum likelihood estimator and at the same time produce smaller mean squared error. We study the sta-tistical properties of the proposed estimator and a simulation study has been conducted to compare the performance of the estimators in the smaller mean squared error sense. Finally, Swedish traffic fatality data are analyzed to show the beneft of the proposed method.

In this paper, we suggest a unit root test for a system of equations using a spectral variance decomposition method based on the Maximal Overlap Discrete Wavelet Transform. We obtain the limiting distribution of the test statistic and study its small sample properties using Monte Carlo simulations. We find that, for multiple time series of small lengths, the wavelet-based method is robust to size distortions in the presence of cross-sectional dependence. The wavelet-based test is also more powerful than the Cross-sectionally Augmented Im et al. unit root test (Pesaran, M. H. 2007. "A Simple Panel Unit Root Test in the Presence of Cross-section Dependence." Journal of Applied Econometrics 22 (2): 265-312.) for time series with between 20 and 100 observations, using systems of 5 and 10 equations. We demonstrate the usefulness of the test through an application on evaluating the Purchasing Power Parity theory for the Group of 7 countries and find support for the theory, whereas the test by Pesaran (Pesaran, M. H. 2007. "A Simple Panel Unit Root Test in the Presence of Cross-section Dependence." Journal of Applied Econometrics 22 (2): 265-312.) finds no such support. 

This article introduces a two-step calibration technique for the inverse relationship between study variable and auxiliary variable along with the double use of the auxiliary variable. In the first step, the calibration weights and design weights are set proportional to each other for a given sample. While in the second step, the constant of proportionality is to be obtained on the basis of some different objectives of the investigation viz. bias reduction or minimum Mean Squared Error (MSE) of the proposed estimator. Many estimators based on inverse relationship between x and y have been already developed and are considered to be special cases of the proposed estimator. Properties of the proposed estimator is discussed in details. Moreover, a simulation study has also been conducted to compare the performance of the proposed estimator under Simple Random Sampling Without Replacement (SRSWOR) and Lahiri-Midzuno (L-M) sampling design in terms of percent relative bias and MSE. The benefits of two-step calibration estimator are also demonstrated using real life data.

This article introduces two different non-parametric wavelet-based panel unit-root tests in the presence of unknown structural breaks and cross-sectional dependencies in the data. These tests are compared with a previously suggested non-parametric wavelet test, the parameteric Im-Pesaran and Shin (IPS) test and a Wald type of test. The results from the Monte Carlo simulations clearly show that the new wavelet-ratio tests are superior to the traditional tests both in terms of size and power in panel unit-root tests because of its robustness to cross-section dependency and structural breaks. Based on an empirical Central American panel application, we can, in contrast to previous research (where bias due to structural breaks is simply disregarded), find strong, clear-cut support for purchasing power parity (PPP) in this developing region.

In numerous application areas, when the response variable is continuous, positively skewed, and well fitted to the inverse Gaussian distribution, the inverse Gaussian regression model (IGRM) is an effective approach in such scenarios. The problem of multicollinearity is very common in several application areas like chemometrics, biology, finance, and so forth. The effects of multicollinearity can be reduced using the ridge estimator. This research proposes new ridge estimators to address the issue of multicollinearity in the IGRM. The performance of the new estimators is compared with the maximum likelihood estimator and some other existing estimators. The mean square error is used as a performance evaluation criterion. A Monte Carlo simulation study is conducted to assess the performance of the new ridge estimators based on the minimum mean square error criterion. The Monte Carlo simulation results show that the performance of the proposed estimators is better than the available methods. The comparison of proposed ridge estimators is also evaluated using two real chemometrics applications. The results of Monte Carlo simulation and real applications confirmed the superiority of the proposed ridge estimators to other competitor methods.

This study assesses the performance of two-parameter estimation methods to combat multicollinearity in the Gamma regression model. We derived optimal values for two-parameter estimation methods in the Gamma regression model. Furthermore, we proposed some estimation methods to estimate the shrinkage parameters and these methods improve the efficiency of the two-parameter estimator. We compare the performance of these estimators by means of Monte Carlo simulation study where the mean squared error (MSE) is considered as a performance criterion. Finally, consider a reaction rate data to evaluate the performance of the estimators. The simulation and numerical example results showed that the two-parameter biased estimators have smaller MSE than the maximum likelihood estimator under certain conditions.

This article introduces the almost unbiased gamma ridge regression estimator (AUGRRE) estimator based on the gamma ridge regression estimator (GRRE). Furthermore, some shrinkage parameters are proposed for the AUGRRE. The performance of the AUGRRE by using different shrinkage parameters is compared with the existing GRRE and maximum likelihood estimator. A Monte Carlo simulation is carried out to assess the performance of the estimators where the bias and mean squared error performance criteria are used. We also used a real-life dataset to demonstrate the benefit of the proposed estimators. The simulation and real-life example results show the superiority of AUGRRE over the GRRE and the maximum likelihood estimator for the gamma regression model with collinear explanatory variables.

In this article, we propose some diagnostic techniques for the inverse Gaussian regression model (IGRM), which are appropriate for modeling the response variable that undertakes positively skewed continuous dataset. Moreover, two new diagnostic methods are mainly proposed for the IGRM, which named as covariance ratio (CVR) and Welsch?s distance (WD). The comparison of our proposed methods of influence diagnostics with the existing approaches has been made through Monte Carlo simulation under different factors. In addition, the benefit of the proposed methods is assessed using a real application. Based on the simulation and empirical application results, we observed that the performance of the proposed method is better than the existing methods for detection of influential observations.

Trypanosomiasis (Surra) is a parasitic and zoonotic disease caused by Trypanosoama evansi, transmitted by insect vectors Tabanus and Stomoxys mechanically. The aim of the present study was to determine the therapeutic efficacy of various trypanosidal drugs against trypanosomiasis in Thoroughbred horses. Horses having clinical signs of trypanosomiasis were diagnosed through blood smear through a microscope were selected for this study. The infected horses were divided into three experimental groups for therapeutic trials. Animals in group A were treated with a single dose of quinapyramine sulphate @ 3000mg/ml per 50/kg body weight; group B was treated with a single dose of isometamedium chloride Hydrochloride@ 0.5 mg/2.5 ml of 1% solution per 50/kg body weight; group C was treated with a single dose of diminazene aceturate@ 2360 mg/15 ml per 100/kg. Results revealed that significant (P<0.0001) de cline in the values of erythrocyte counts (RBC), hemoglobin concentration (Hb), packed cell volume (PCV), platelets (PLT) and a significant (P<0.0001) increase in white blood cells (WBC), granulocytes, and monocytes in infected horses as compared to healthy ones. Therapeutic trials indicated that quinapyramine sulphate that showed 100% efficacy at 21th days had significantly higher than isometamedium chloride and diminazene aceturate (95.83 and 75% efficacy, respectively). The hematological parameters of recovered horses were significantly restored to normal values on day 21 after treatment. It is concluded that quinapyramine sulphate is the drug of choice against trypanosomiasis in Thoroughbred horses.

Background: Clostridioides difficile infection (CDI) causes a major burden to individuals and society, yet the impact may vary depending on age, sex, underlying comorbidities and where CDI was acquired (hospital or community). Methods: This Swedish nationwide population-based cohort study (2006–2019) compared all 43,150 individuals with CDI to their 355,172 matched controls (first year and entire follow-up). Negative binomial regression models compared the cumulated length of stay, number of in-hospital admissions, outpatient visits and prescriptions after the first CDI episode expressed as incidence rate ratios (IRR) and 95% confidence intervals for the entire follow-up. Results: Overall, 91.6% of CDI cases were hospital acquired, and 16.8% presented with recurrence(s); 74.8%of cases were ≥ 65 years and 54.2% were women. Compared to individuals without CDI, in-hospital stay rates were 18.01 times higher after CDI (95% CI 17.40–18.63, first-year: 27.4 versus 1.6 days), 9.45 times higher in-hospital admission (95% CI 9.16–9.76, first-year: 2.6 versus 1.3 hospitalisations), 3.94 times higher outpatient visit (95% CI 3.84–4.05, first-year: 4.0 versus 1.9 visits) and 3.39 times higher dispensed prescriptions rates (95% CI 3.31–3.48, first-year: 25.5 versus 13.7 prescriptions). For all outcomes, relative risks were higher among the younger (< 65 years) than the older (≥ 65 years), and in those with fewer comorbidities, but similar between sexes. Compared to those without recurrence, individuals with recurrence particularly showed a higher rate of hospital admissions (IRR = 1.18, 95% 1.12–1.24). Compared to community-acquired CDI, those with hospital-acquired CDI presented with a higher rate of hospital admissions (IRR = 7.29, 95% CI 6.68–7.96) and a longer length of stay (IRR = 7.64, 95% CI 7.07–8.26). Conclusion: CDI was associated with increased health consumption in all affected patient groups. The majority of the CDI burden could be contributed to hospital-acquired CDI (~ 9/10), older patients (~ 3/4) and those with multiple comorbidities (~ 6/10 Charlson score ≥ 3), with 1/5 of the total CDI burden contributed to individuals with recurrence. Yet, relatively speaking the burden was higher among the younger and those with fewer comorbidities, compared to their peers without CDI.

Objective: To assess whether different forms of cognitive social capital increased the relative probability of testing for sexually transmitted infections (STIs) among young men living in Stockholm, Sweden.

Methods: A population-based cross-sectional study was conducted in 2017 with men aged 20–29 years living in Stockholm County, Sweden (n = 523). The main outcome was STI testing patterns (never tested, tested only within a 12-month period, tested only beyond a 12-month period, repeatedly tested). The main exposure were two forms of cognitive social capital: social support (having received help, having someone to share inner feelings with) and institutionalized trust (in school, healthcare, media). Data were analyzed using weighted multivariable multinomial logistic regression to obtain adjusted weighted relative probability ratio (aRPR).

Results: After adjusting for confounding factors, receiving help (aRPR: 5.2, 95% CI: 1.7–16.2) and having someone to share inner feelings with (aRPR: 3.1, 95% CI: 1.2–7.7) increased the relative probabilities of young men testing for STIs, but only for those testing beyond a 12-month period. Trust in media increased the relative probability of STI testing for those testing only within a 12-month period (aRPR: 2.6, 95% CI: 1.1–6.1) and for those testing repeatedly (aRPR: 3.6, 95% CI: 1.5–8.8).

Conclusion: Young men in Stockholm County exhibit distinct STI testing patterns. Social support and trust in media were factors that increased the probability of being tested for STIs, with this effect varying according to the young men's STI testing pattern. Further studies are required to explore how trust in media might promote STI testing in this population.

Common principal components (CPCs) are often estimated using maximum likelihood estimation through an algorithm called the Flury–Gautschi (FG) Algorithm. Krzanowski proposed a simpler estimation method via a principal component analysis of a weighted sum of the sample covariance matrices. These methods are compared for real-world datasets and in a Monte Carlo simulation. The real-world data is used to compare the selection of a common eigenvector model and the estimated coefficients. The simulation study investigates how the accuracy of the methods is affected by autocorrelation, the number of covariance matrices, dimensions, and sample sizes for multivariate normal and chi-square distributed data. The findings in this article support the use of Krzanowski’s method in situations where the CPC assumption is appropriate. 

This thesis consists of four papers, all exploring some aspect of common principal component analysis (CPCA), the generalization of PCA to multiple groups. The basic assumption of the CPC model is that the space spanned by the eigenvectors is identical across several groups, whereas eigenvalues associated with the eigenvectors can vary. CPCA is used in essentially the same areas and applications as PCA.

The first paper compares the performance of the maximum likelihood and Krzanowski’s estimators of the CPC model for two real-world datasets and in a Monte Carlo simulation study. The simplicity and intuition of Krzanowski's estimator and the findings in this paper support and promote the use of this estimator for CPC models over the maximum likelihood estimator.

Paper number two uses CPCA as a tool for imposing restrictions on system-wise regression models. The paper contributes to the field by proposing a variety of explicit estimators, deriving their properties and identifying the appropriate amount of smoothing that should be imposed on the estimator. 

In the third paper, a generalization of the fixed effects PCA model to multiple populations in a CPC environment is proposed. The model includes mainly geometrical, rather than probabilistic, assumptions, and is designed to account for any possible prior information about the noise in the data to yield better estimates, obtained by minimizing a least squares criterion with respect to a specified metric.

The fourth paper survey some properties of the orthogonal group and the associated Haar measure on it. It is demonstrated how seemingly abstract results contribute to applied statistics and, in particular, to PCA.

The focus of this thesis is the common principal component (CPC) model, the generalization of principal components to several populations. Common principal components refer to a group of multidimensional datasets such that their inner products share the same eigenvectors and are therefore simultaneously diagonalized by a common decorrelator matrix. Common principal component analysis is essentially applied in the same areas and analysis as its one-population counterpart. The generalization to multiple populations comes at the cost of being more mathematically involved, and many problems in the area remains to be solved.

This thesis consists of three individual papers and an introduction chapter.In the first paper, the performance of two different estimation methods of the CPC model is compared for two real-world datasets and in a Monte Carlo simulation study. The second papers show that the orthogonal group and the Haar measure on this group plays an important role in PCA, both in single- and multi-population principal component analysis. The last paper considers using common principal component analysis as a tool for imposing restrictions on system-wise regression models. When the exogenous variables of a multi-dimensional model share common principal components, then each of the marginal models in the system is, up to their eigenvalues, identical. They henceform a class of regression models situated in between the classical seemingly unrelated regressions, where each set of explanatory variables is unique, and multivariate regression, where each marginal model shares the same common set of regressors.

This paper explores multivariate data using principal component analysis (PCA). Traditionally, two different approaches to PCA have been considered, an algebraic descriptive one and a probabilistic one. Here, a third type of PCA approach, lying somewhere between the two traditional approaches, called the fixed effects PCA model, is considered. This model includes mainly geometrical, rather than probabilistic assumptions, such as the optimal choice of dimensionality and metric. The model is designed to account for any possible prior information about the noise in the data to yield better estimates. Parameters are estimated by minimizing a least-squares criterion with respect to a specified metric. A suggestion of how the fixed effects PCA estimates can be improved in a common principal component (CPC) environment is made. If the CPC assumption is fulfilled, then the fixed effects PCA model can consider more information by incorporating common principal component analysis (CPCA) theory into the estimation procedure. 

Agencies that regulate electricity providers often apply nonparametric data envelopment analysis (DEA) to assess the relative efficiency of each firm. The reliability and validity of DEA are contingent upon selecting relevant input variables. In the era of big (wide) data, the assumptions of traditional variable selection techniques are often violated due to challenges related to high-dimensional data and their standard empirical properties. Currently, regulators have access to a large number of potential input variables. Therefore, our aim is to introduce new machine learning methods for regulators of the energy market. We also propose a new two-step analytical approach where, in the first step, the machine learning-based adaptive least absolute shrinkage and selection operator (ALASSO) is used to select variables and, in the second step, selected variables are used in a DEA model. In contrast to previous research, we find, by using a more realistic data-generating process common for production functions (i.e., Cobb–Douglas and Translog), that the performance of different machine learning techniques differs substantially in different empirically relevant situations. Simulations also reveal that the ALASSO is superior to other machine learning and regression-based methods when the collinearity is low or moderate. However, in situations of multicollinearity, the LASSO approach exhibits the best performance. We also use real data from the Swedish electricity distribution market to illustrate the empirical relevance of selecting the most appropriate variable selection method.

Interventions to improve health among adults with intellectual disabilities (ID) living in community residences are needed as unhealthy behaviors, obesity, and chronic diseases are more common in this group than in the general population. This study evaluated effectiveness of a structural health intervention, a study circle for paid carers aiming to improve health promotion work routines for residents, and explored barriers and facilitators in the implementation process. A quasi-experimental design was used. Eight municipalities with 84 community residences agreed to participate with 70 of these completing the study. A 26-item questionnaire was used regarding staff work routines in three domains (general health promotion, food and meals, physical activity) and a total score to evaluate effectiveness. An inductive qualitative method was used to explore barriers and facilitators in the implementation process. The intervention group (n = 42 residences) improved their health promoting work routines significantly more than the comparison group (n = 28 residences) in the domains of general health promotion (p =.05), physical activity (p =.02), and for the total score (p =.002), but no significant change was found in the food and meal domain (p =.11). Regarding barriers and facilitators in the implementation process, a “Need for a supportive structure and key persons with a mandate to act,” was identified as an overarching theme. Barriers and facilitators were identified within four categories: (1) characteristics of the study circle, (2) staff capacity, (3) organizational capacity, and (4) external support. This study provides evidence that a structural intervention targeting staff in community residences for people with ID can improve health promoting work routines and that the results might be generalizable. If disseminated on a wider scale, this intervention has the potential of improving health and preventing obesity and other chronic diseases in adults with ID. 

Background: Despite numerous risk factors and serious consequences, little is known about metabolic dysfunction-associated steatotic liver disease (MASLD) at population level in Africa. Aim: The aim of the study was to estimate the prevalence and risk factors of MASLD in people living with and without HIV in Uganda. Methods: We collected data from 37 communities in South Central Uganda between May 2016 and May 2018. We estimated MASLD prevalence using the fatty liver index and advanced liver fibrosis using the dynamic aspartate-to-alanine aminotransferase ratio. We collected additional data on sociodemographics, HIV and cardiovascular disease (CVD) risk factors. We used multivariable logistic regression to determine the association between HIV, CVD risk factors and MASLD. Results: We included 759 people with HIV and 704 HIV-negative participants aged 35–49. MASLD prevalence was 14% in women and 8% in men; advanced liver fibrosis prevalence was estimated to be <1%. MASLD prevalence was more common in women (15% vs. 13%) and men (9% vs. 6%) with HIV. Being female (odds ratio [OR] = 2.1; 95% confidence interval [CI] = 1.4–3.3) was associated with a higher odds of MASLD after adjustment for confounders; HIV infection was borderline associated with MASLD (OR = 1.4; 95% CI: 1.0–2.0). Conclusions: In a relatively young cohort in Uganda, 14% of women and 8% of men had MASLD. There was an indication of an association between HIV and MASLD in multivariable analysis. These data are the first to describe the population-level burden of MASLD in sub-Saharan Africa using data from a population-based cohort.

Our research examines the role of airports in regional development. Specifically, we examine two things: (1) the factors associated with whether or not a metro will have an airport, and (2) the effect of airport activities on regional economic development. Based on multiple regression analysis for U.S. metros, our research generates four key findings. First, airports are more likely to be located in larger metros with higher shares of cultural workers and warmer winters. Second, airports add significantly to regional development measured as economic output per capita. Third, the effect of airports on regional development occurs through two channels—their capacity to move both people and cargo, with the former being somewhat more important. Fourth, the impact of airports on regional development varies with their size and scale.

We suggest a wavelet filtering technique as a remedy to the problem of measurement errors when testing for cointegration using Johansen’s (1988) likelihood ratio test. Measurement errors, which more or less are always present in empirical economic data, essentially indicates that the variable of interest (the true signal) is contaminated with noise, which may induce biased and inconsistent estimates and erroneous inference. Our Monte Carlo experiments demonstrate that measurement errors distort the statistical size of Johansen’s cointegration test in finite samples; the test is significantly oversized. A contribution and major finding of this article is that the proposed wavelet-based technique significantly improves the statistical size of the traditional Johansen test in small and medium sized samples. Since Johansen’s test is a standard cointegration test, and we demonstrate that the constantly present measurement errors in empirical data over sizes the test, this simple alteration can be used in most situations with more reliable finite sample inference. We empirically examine the long-run relation between CO2 emissions and the real GDP in the G7 countries. The traditional Johansen tests provide evidence of an equilibrium relation for Canada and weak evidence for the US. However, the suggested size-unbiased wavelet-filtering approach consistently indicates no evidence of cointegration for all six countries.

This paper applies Monte Carlo simulations to evaluate the size and power properties in the presence of a structural break, for the standard Augmented Dickey-Fuller (ADF) test versus nonlinear exponential smooth transition autoregressive unit root tests. The break causes the tests to be undersized, and the statistical power considerably decreases. Moreover, the effect is intensified in small samples and very much increased for more persistent nonlinear series. As a remedy, we modify the standard ADF and exponential smooth transition autoregressive unit root tests in order to adjust for a structural break. This improves both the power and the size considerably, even though the empirical size still is lower than the nominal one. More persistent series are more affected by structural breaks, and the new tests are most powerful under the existence of a rather persistent nonlinear data generating process (which is an empirically relevant and common type of data generating process). The proposed tests are applied to investigate mean reversion in the real effective exchange rates of 5 East and Southeast Asian countries, taking into account the structural change in exchange rate regime brought about by the 1997 Asian financial crisis. The empirical findings corroborate our simulation results; the modified more powerful tests are able to reject the unit root in all 5 countries, whereas the tests that do not consider the structural break could only reject in one of these cases.

Background: Regular physical activity reduces the risk of depression onset and is an effective treatment for mood disorders. Recent studies have reported that sedentary behavior (SB) increases the risk of depression in adults, but relationships of different types of SBs with depression have not been examined systematically. We explored longitudinal relationships of passive (e.g. watching TV) and mentally-active (e.g. office-work) SBs with incident major depressive disorder (MDD).

Methods: Self-report questionnaires were completed by 40,569 Swedish adults in 1997; responses were linked to clinician-diagnosed MDD obtained from medical registers until 2010. Relationships between passive, mentally-active and total SBs with incident MDD were explored using survival analysis with Cox proportional hazards regression. Models controlled for leisure time moderate-vigorous physical activity and occupational physical activity. Moderating effects of gender were examined.

Results: In fully-adjusted models, including only non-depressed adults at baseline, those reporting ≥ 3 h of mentally-active SBs on a typical day (versus < 3 h) had significant lower hazards of incident MDD at follow-up (HR = 0.74, 95% CI = 0.58–0.94, p = 0.018). There was a non-significant positive relationship of passive SBs with incident MDD (HR = 1.20, 95% CI = 0.96–1.52, p = 0.106). The association between total SBs (passive and mentally-active combined) was not significant (HR = 0.91, 95% CI = 0.75–1.10, p = 0.36). Gender did not moderate these associations.

Limitations: Physical activity and SBs were self-reported.

Conclusion: Mentally-active SBs may have beneficial effects on adults’ mental well-being. These effects are largely independent of habitual physical activity levels. 

BACKGROUND: Growing evidence indicates that gender-based violence (GBV) increased during COVID-19. We investigated self-reported impact of the pandemic on GBV at community, household and intimate partner (IPV) levels among young people and its associations with psychosocial wellbeing, i.e., COVID-related stressors and mental health.

METHODS: Cross-sectional data were drawn from a survey with young people ages 13-24 (N = 536) living with HIV (YPLWH) and without HIV (YPLWoH), in peri-urban Cape Town, South Africa. The survey, conducted February-October 2021, examined the impact of the initial lockdown on experience and perceived changes in GBV at each level, and pandemic-related psychosocial wellbeing. Descriptive statistics and binomial and multinomial regression analyses were conducted to illustrate exposure and perceived changes in GBV since lockdown, and their association with COVID-related stress factors (e.g., social isolation, anxiety about COVID), mental health (e.g., depression, anxiety), and other risk factors (e.g., age, gender, socioeconomic status) by HIV status.

RESULTS: Participants were 70% women with mean age 19 years; 40% were living with HIV. Since lockdown, YPLWoH were significantly more likely than YPLWH to perceive community violence as increasing (45% vs. 28%, p < 0.001), and to report household violence (37% vs. 23%, p = 0.006) and perceive it as increasing (56% vs. 27%, p = 0.002) (ref: decreasing violence). YPLWoH were also more likely to report IPV experience (19% vs. 15%, p = 0.41) and perception of IPV increasing (15% vs. 8%, p = 0.92). In adjusted models, COVID-related stressors and common mental health disorders were only associated with household violence. However, indicators of economic status such as living in informal housing (RRR = 2.07; 95% CI = 1.12-3.83) and food insecurity (Community violence: RRR = 1.79; 95% CI = 1.00-3.20; Household violence: RRR = 1.72; 95% CI = 1.15-2.60) emerged as significant risk factors for exposure to increased GBV particularly among YPLWoH.

CONCLUSIONS: Findings suggest that for young people in this setting, GBV at community and household levels was more prevalent during COVID-19 compared to IPV, especially for YPLWoH. While we found limited associations between COVID-related stressors and GBV, the perceived increases in GBV since lockdown in a setting where GBV is endemic, and the association of household violence with mental health, is a concern for future pandemic responses and should be longitudinally assessed.

This paper argues that traditional measures of innovation as a univariate phenomenon may not be dynamic enough to adequately describe the complex nature of innovation. Consequently, the purpose is to develop a multidimensional index of innovation that is able to reflect innovation enablers and outputs. The index may then be used (i) to assess and quantify temporal changes of innovation, (ii) to describe regional differences and similarities of innovation, and (iii) serve as exogenous variables to analyze the importance of innovation for other economic phenomena. Our index is defined in a four-dimensional space of orthogonal axes. An empirical case study is used for demonstration of the index, where 44 variables are collected for all municipalities in Sweden. The index spanning the four-dimensional innovation comprises size, accessibility, firm performance, and agglomeration. The proposed index offers a new way of defining and analyzing innovation and should have a wide range of important applications in a world where innovation is receiving a great deal of recognition.

In this paper we generalize four tests of multivariate linear hypothesis to panel data unit root testing. The test statistics are invariant to certain linear transformations of data and therefore simulated critical values may conveniently be used. It is demonstrated that all four tests remains well behaved in cases of where there are heterogeneous alternatives and cross-correlations between marginal variables. A Monte Carlo simulation is included to compare and contrast the tests with two well-established ones.

Recent outbreaks of Cutaneous Leishmaniasis (CL) in Waziristan make the disease a public health concern in Khyber Pakhtunkhwa (KPK) province, Pakistan. The awareness and behavior of local community towards the disease is an important factor towards effective control and management of CL in endemic areas of Pakistan. A cross-sectional community based survey was piloted in new emerging district of North Waziristan Agency (KPK province), Pakistan from August 2019- February 2020. The study aimed to examine the Knowledge, Attitude and Practices (KAP) of the local community members regarding CL. The results revealed that majority of the participants were male. Only 48.2% participants have knowledge about CL and the respondents had a moderate knowledge of CL vector and the disease. Few of the respondents were aware that CL is caused by sand flies, their breeding place, biting time, transmission of CL and control measures. Skin infection and sand-flies were the main disease symptoms and disease vector were known to some of the respondents. Most of the respondents showed positive attitude towards disease seriousness and believed that the disease could be cured and can be treated through modern medicines. Admission to hospitals, cleanliness and use of bed nets were the treatment measures for the disease in suspected patients, whereas some believed that the use of bed nets could be helpful in preventing the leishmaniasis. Moderate knowledge of the CL and its transmission in the study area emphasize the need to initiate health education and awareness campaigns to reduce the disease risk and burden in this highly endemic area in near future.

Background and Aim: Mastitis has been identified as the most prevalent and economically imperative disease among dairy animals. Thus, understanding its common bacterial pathogens and risk factors is necessary to improve udder health at herd, region, or country level. However, scientific research on caprine mastitis, especially on Beetal breed, has remained to be insufficient in Pakistan. Therefore, this study aimed to evaluate the epidemiology and antibiogram assay of common mastitis-causing bacterial agents, that is, Staphylococcus, Streptococcus, and Escherichia coli, in dairy goats.

Materials and Methods: In total, 500 Beetal goats, irrespective of age and those that were not treated with any kind of antimicrobial agents during the past 120 h, were screened using California Mastitis Test in Pattoki, Kasur District, whereas epidemiological factors were recorded. The milk samples of mastitic goats were then collected and processed using standard methods. Each sample was primarily cultured on nutrient agar. Using a specific medium, each bacterial colony was separated using several streak methods. Six antibiotic disks belonging to different antibiotic groups were used for antibiogram profiling of bacterial isolates. Chi-square test was used to assess the association of baseline characteristics and mastitis occurrence. Meanwhile, multivariable logistic regression (p<0.001) was utilized to determine the risk factors associated with positive and negative dichotomous outcome of mastitis.

Results: The results revealed that the overall prevalence of goat mastitis was 309 (61.8%), in which 260 (52%) and 49 (9.8%) cases were positive for subclinical mastitis (SCM) and clinical mastitis (CM), respectively. Streptococcus and E. coli were found to be the predominant isolates causing SCM and CM, respectively (p<0.001). It was observed that amoxicillin+clavulanic acid was highly sensitive to isolates of Staphylococcus and Streptococcus and ceftiofur sodium to isolates of Streptococcus and E. coli, while enrofloxacin was found to be sensitive to isolates of Streptococcus and E. coli. Risk factors such as herd structure, deworming, vaccination, presence of ticks, use of teat dip and mineral supplements, feeding type, age, parity, housing, blood in the milk, milk leakage, milk taste, and milk yield were found to have the strongest association with mastitis occurrence, while ease of milking has moderate association.

Conclusion: In the area examined, cases of SCM were found to be higher compared with that of CM, and ceftiofur sodium has been identified as the preferred treatment in both clinical and subclinical forms of caprine mastitis in Beetal goats. Risk factors for mastitis that was identified in this study can form the basis for the creation of an udder health control program specific for dairy goats. We hope our findings could raise awareness of the risk factors and treatment approaches for common mastitis-causing bacterial agents. 

Objective

To evaluate changes in Ebola-related knowledge, attitudes and prevention practices during the Sierra Leone outbreak between 2014 and 2015.

Methods

Four cluster surveys were conducted: two before the outbreak peak (3499 participants) and two after (7104 participants). We assessed the effect of temporal and geographical factors on 16 knowledge, attitude and practice outcomes.

Findings

Fourteen of 16 knowledge, attitude and prevention practice outcomes improved across all regions from before to after the outbreak peak. The proportion of respondents willing to: (i) welcome Ebola survivors back into the community increased from 60.0% to 89.4% (adjusted odds ratio, aOR: 6.0; 95% confidence interval, CI: 3.9–9.1); and (ii) wait for a burial team following a relative’s death increased from 86.0% to 95.9% (aOR: 4.4; 95% CI: 3.2–6.0). The proportion avoiding unsafe traditional burials increased from 27.3% to 48.2% (aOR: 3.1; 95% CI: 2.4–4.2) and the proportion believing spiritual healers can treat Ebola decreased from 15.9% to 5.0% (aOR: 0.2; 95% CI: 0.1–0.3). The likelihood respondents would wait for burial teams increased more in high-transmission (aOR: 6.2; 95% CI: 4.2–9.1) than low-transmission (aOR: 2.3; 95% CI: 1.4–3.8) regions. Self-reported avoidance of physical contact with corpses increased in high but not low-transmission regions, aOR: 1.9 (95% CI: 1.4–2.5) and aOR: 0.8 (95% CI: 0.6–1.2), respectively.

Conclusion

Ebola knowledge, attitudes and prevention practices improved during the Sierra Leone outbreak, especially in high-transmission regions. Behaviourally-targeted community engagement should be prioritized early during outbreaks.

Background: Vaccination against Ebolavirus is an emerging public health tool during Ebola Virus Disease outbreaks. We examined demand issues related to deployment of Ebolavirus vaccine during the 2014–2015 outbreak in Sierra Leone. Methods: A cluster survey was administered to a population-based sample in December 2014 (N = 3540), before any Ebola vaccine was available to the general public in Sierra Leone. Ebola vaccine demand was captured in this survey by three Likert-scale items that were used to develop a composite score and dichotomized into a binary outcome to define high demand. A multilevel logistic regression model was fitted to assess the associations between perceptions of who should be first to receive an Ebola vaccine and the expression of high demand for an Ebola vaccine. Results: The largest proportion of respondents reported that health workers (35.1%) or their own families (29.5%) should receive the vaccine first if it became available, rather than politicians (13.8%), vaccination teams (9.8%), or people in high risk areas (8.2%). High demand for an Ebola vaccine was expressed by 74.2% of respondents nationally. The odds of expressing high demand were 13 times greater among those who said they or their families should be the first to take the vaccine compared to those who said politicians should be the first recipients (adjusted odds ratio [aOR] 13.0 [95% confidence interval [CI] 7.8–21.6]). The ultra-brief measure of the Ebola vaccine demand demonstrated acceptable scale reliability (Cronbach's α = 0.79) and construct validity (single-factor loadings > 0.50). Conclusion: Perceptions of who should be the first to get the vaccine was associated with high demand for Ebola vaccine around the peak of the outbreak in Sierra Leone. Using an ultra-brief measure of Ebola vaccine demand is a feasible solution in outbreak settings and can help inform development of future rapid assessment tools. 

Objective: To understand the associations of COVID-19 cases and deaths with policy stringency globally and regionally.

Methods: We modeled the marginal effects of new COVID-19 cases and deaths on policy stringency (scored 0–100) in 175 countries and territories, adjusting for gross domestic product (GDP) per capita and health expenditure (% of GDP). Time periods examined were March–August 2020, September 2020– February 2021, and March–August 2021.

Results: Policy response to new cases and deaths was faster and more stringent early in the COVID-19 pandemic (March–August 2020) compared to subsequent periods. New deaths were more strongly associated with stringent policies than new cases. In an average week, 1 new death per 100,000 people was associated with a stringency increase of 2.1 units in March–August 2020, 1.3 units in September 2020–February 2021, and 0.7 units in March–August 2021. New deaths in Africa and the Western Pacific were associated with more stringency than in other regions. Higher health expenditure was associated with less stringent policies. GDP per capita did not have consistent patterns of associations with stringency.

Conclusions: Our findings demonstrate the need for enhanced mortality surveillance to ensure policy alignment during health emergencies. Countries that invest less of their GDP in health are inclined to enact stringent policies during health emergencies than countries with more significant health expenditure.

Background: New data on COVID-19 may influence the stringency of containment policies, but these potential effect are not understood. We aimed to understand the associations of new COVID-19 cases and deaths with policy stringency globally and regionally.

Methods: We modelled the marginal effects of new COVID-19 cases and deaths on policy stringency (scored 0-100) in 175 countries and territories, adjusting for gross domestic product (GDP) per capita and health expenditure (% of GDP), and public expenditure on health. The time periods examined were March to August 2020, September 2020 to February 2021, and March to August 2021.

Results: Policy response to new cases and deaths was faster and more stringent early in the COVID-19 pandemic (March to August 2020) compared to subsequent periods. New deaths were more strongly associated with stringent policies than new cases. In an average week, one new death per 100 000 people was associated with a stringency increase of 2.1 units in the March to August 2020 period, 1.3 units in the September 2020 to February 2021 period, and 0.7 units in the March to August 2021 period. New deaths in Africa and the Western Pacific were associated with more stringency than in other regions. Higher health expenditure as a percentage of GDP was associated with less stringent policies. Similarly, higher public expenditure on health by governments was mostly associated with less stringency across all three periods. GDP per capita did not have consistent patterns of associations with stringency.

Conclusions: The stringency of COVID-19 policies was more strongly associated with new deaths than new cases. Our findings demonstrate the need for enhanced mortality surveillance to ensure policy alignment during health emergencies. Countries that invest less in health or have a lower public expenditure on health may be inclined to enact more stringent policies. This new empirical understanding of COVID-19 policy drivers can help public health officials anticipate and shape policy responses in future health emergencies.

This study investigates Granger causality and instantaneous causality between financial development and economic development for 76 economies of four different income levels. The main novelty of the study is that it fills a gap in existing studies on the relationship between financial development and economic development by employing wavelet analysis, which enables us to study the varying timescale relationships of the variables. Among the findings is that at the scale of 4–8 years, it is more common for financial development and economic development to support each other once a country achieves at least lower-middle-income status.

This article examines whether the purchasing power parity (PPP) theory holds or not for the economies in different developing regions located in Africa, Asia and Latin America. In order to investigate this issue, a nonlinear panel unit root test is used to determine if some or all of the real exchange rates in a panel follow a stationary exponential smooth transition autoregressive process. By applying the nonlinear panel unit root test, our results demonstrate an empirical support for the theory of PPP for the economies in developing regions.

The objective of this paper is to re-examine the relationship between real oil prices and teal effective exchange rates (REER) for major oil-exporting countries with floating exchange rates. We apply the wavelet-based principles of Gallegati et al. (2016) using monthly data for the period 1996 to 2015. In contrast to many previous studies, our results support the theoretically expected positive nexus between the real oil prices and REER for our dataset. This (theoretically-expected) positive relationship is stronger at the larger time scales (that is, at the 4-8 and 8-16 month wavelet scales) compared to the smaller time scales (that is, at the 1-2 and 2-4 month wavelet scales). The findings of this study therefore add to the existing literature, since they disentangle the specific relationship between oil prices and exchange rates at different time scales, which has important policy implications.

Beta regression has become a popular tool for performing regression analysis on chemical, environmental, or biological data in which the dependent variable is restricted to the interval [0, 1]. For the first time, in this paper, we propose a Liu estimator for the beta regression model with fixed dispersion parameter that may be used in several realistic situations when the degree of correlation among the regressors differs. First, we show analytically that the new estimator outperforms the maximum likelihood estimator (MLE) using the mean square error (MSE) criteria. Second, using a 'simulation study, we investigate the properties in finite samples of six different suggested estimators of the shrinkage parameter and compare it with the MLE. The simulation results indicate that in the presence of multicollinearity, the Liu estimator outperforms the MLE uniformly. Finally, using an empirical application on chemical data, we show the benefit of the new approach to applied researchers.

This paper concerns the problem of estimating the Mahalanobis distance when the dimension of the data matrix is comparable to the sample size. Two different ridge-shrinkage estimators are considered and estimators of related risk functions are derived. The properties of these point estimators are investigated in terms of excess risk and bias relative to the traditional estimator.

Model selection criteria are often used to find a “proper” model for the data under investigation when building models in cases in which the dependent or explained variables are assumed to be functions of several independent or explanatory variables. For this purpose, researchers have suggested using a large number of such criteria. These criteria have been shown to act differently, under the same or different conditions, when trying to select the “correct” number of explanatory variables to be included in a given model; this, unfortunately, leads to severe problems and confusion for researchers. In this paper, using Monte Carlo methods, we investigate the properties of four of the most common criteria under a number of realistic situations. These criteria are the adjusted coefficient of determination ((Formula presented.)-adj), Akaike’s information criterion (AIC), the Hannan–Quinn information criterion (HQC) and the Bayesian information criterion (BIC). The results from this investigation indicate that the HQC outperforms the BIC, the AIC and the (Formula presented.)-adj under specific circumstances. None of them perform satisfactorily, however, when the degree of multicollinearity is high, the sample sizes are small or when the fit of the model is poor (i.e., there is a low (Formula presented.). In the presence of all these factors, the criteria perform very badly and are not very useful. In these cases, the criteria are often not able to select the true model. 

This article investigates the performance of several residuals for the Cox proportional hazards regression model to diagnose the influential observations. The standardized and adjusted forms of residuals are proposed for Cox proportional hazards regression model. In addition, Cook's distance is proposed for both standardized and adjusted residuals. The assessment of different residuals for the identification of influential observations is made through the Monte Carlo simulation. A real dataset of bone marrow transplant Leukaemia is analyzed to show the benefit of the proposed methods. Simulation and application results show that the standardized and adjusted residuals based on the Cox-Snell method perform best for the detection of influential points. Furthermore, the standardized, and adjusted Martingale and deviance residuals work better when the sample size is large.

The current research aimed to monitor and assess the heavy metal contamination in the surface water of 53 sampling sites along the selected rivers using principal component analysis and cluster analysis. For this purpose, both physiochemical parameters such as the temperature (T), the potential of hydrogen (pH), total dissolved solids (TDS) and electroconductivity (EC), and heavy metals such as iron (Fe), chromium (Cr), nickel (Ni), cadmium (Cd), lead (Pb) and arsenic (As) are analyzed as potential water contaminants. The average values of pH, TDS, EC and T are found at 7.75, 70.89 mg/L, 139.11 µs/cm and 20.29 °C, respectively, and heavy metals including Cr, Ni, Cd, Pb, As and Fe are observed at 0.04, 0.04, 0.04, 0.03, 0.001 and 0.04 mg/L, respectively. Moreover, it is found that in both rivers hazardous metals, including Cr (100%), Cd (92.30%), Pb (100%), Ni (100%) and Fe (91%), exceed the permissible limits of the WHO.

This paper proposes several estimators for estimating the ridge parameter k based for Poisson ridge regression (RR) model. These estimators have been evaluated by means of Monte Carlo simulations. As performance criteria, we have calculated the mean squared error (MSE), the mean value and the standard deviation of k. The first criterion is commonly used, while the other two have never been used when analyzing Poisson RR. However, these performance criterion are very informative because, if several estimators have an equal estimated MSE then those with low average value and standard deviation of k should be preferred. Based on the simulated results we may recommend some biasing parameters which may be useful for the practitioners in the field of health, social and physical sciences.