He hurts her womb: Physical-sexual violence and pregnancy complications among women in Afghanistan

Background: Though some studies show the association between intimate partner violence and pregnancy complications in developed countries, the association remains understudied in less developed and low-income settings. This study examines the association of physical and sexual violence with pregnancy complications among women in Afghanistan. Methods: This study used the data from the 2015 Afghanistan Demographic and Health Survey (AfDHS). The analysis included 7229 women aged between 15 and 49 and used logistic regression to show the association of physical and sexual violence with pregnancy compilations. The analysis controlled for some potential variables and followed complex survey design factors such as strata, clusters, and survey weights. Results: Fully adjusted regression model shows that the women who experienced physical violence were 21% (adjusted odds ratio [OR]=1.21; confidence interval [CI]=0.98, 1.50; P<0.1) more likely to endure pregnancy complications compared to those who did not face the violence. Likewise, the women experiencing sexual violence were 89% (adjusted OR=1.89; CI=1.37, 2.62; P<0.01) higher to face pregnancy complications than those who did not face any of sexual violence. More specifically, physically and sexually violated women were highly prey to the complications that increased with the increment of the violence. Conclusion: This study adds that policymakers may formulate policies for ensuring 3C (caring couple counselling) by readdressing couple relationships, raising gender rights and awareness, providing reproductive health literacy, and increasing mental health awareness during pregnancy.

COVID-19 Vaccine Intentions Within the Medicare Population

COVID-19 vaccine intentions by older adults reflect individual care seeking behavior and medical system trust and broader systemic cultural shifts related to vaccine hesitancy. The purpose of this paper is to examine the October wave of the rapid response panel survey fielded by the Centers for Medicare and Medicaid Services (CMS) to track and monitor the effects of the pandemic within the Medicare population. With a sample size of 9686 Medicare beneficiaries, the calculated statistics use replicate weights to adjust for the complex survey sample design and balanced repeated replication using Fay’s adjustment of 0.3 for variance estimation. When asked about the likelihood of getting the COVID-19 vaccine if one were available, 58 percent of the Medicare population definitely or probably intended to get the vaccine, 16 percent expressed they would probably or definitely not, and 26 percent were not sure. Black or Hispanic Medicare beneficiaries were significantly more likely to express they would probably not or definitely not get the vaccine than White, non-Hispanic Medicare beneficiaries. Distrust of what government says about the vaccine and concern about the safety or side effects were the most common reasons for not intending to get the vaccine. Those expressing intentions to not get the COVID-19 vaccine in the October 2020 survey wave were more likely to lack access to the internet, which is a potential systematic barrier if they changed their intentions following the FDA approvals of the COVID-19 vaccines and more information became available in the winter and spring of 2021.

Risk Factors for Intimate Partner Violence Victimisation

<p>Intimate partner violence (IPV) is a significant issue in New Zealand. Reported rates of IPV for New Zealand over a lifetime are estimated at 26% for women 18% for men. Over a 12 month period reported rates of IPV was reported at three percent for women and one point eight percent for men (Morris and Reilly, 2003). This thesis set out to answer the following research questions:1. What factors are strong predictors for intimate partner violence victimisation? 2. Is victim ethnicity factored out when variables such as SES, income and/or employment status are taken into account? This thesis analysed complex survey data from New Zealand National Survey of Crime and Victimisation Survey 2001 (NZNSCV). Logistic regression analysis was use to investigate the risk factors for victimisation reported over a lifetime and reported in the previous 12 month period (2000). Explanatory variables were selected from existing research that identified key variables for analysis. In answer to the research questions: 1. The strong predictors for IPV over a lifetime were Maori, people aged 25-39 years, females, solo parents, over-crowded households, those on socialwelfare benefits, and those divorced orseparated. For 12 month prevalence of IPV strong predictors were those aged 15-24 years, Maori, lower NZSEI groups, solo parents with children, those in defacto relationships and those who do not own or rent a house. 2. Ethnicity is strongly significant in explaining victimisation even when other variables are factored into the model. In particular Maori reported to be twice as likely to be victims of IPV over a lifetime and three-and-a-half times more likely over the past 12 month period (Morris and Riley). This difference in reporting was not as large when other factors where factored in, however Maori remained at a significantly higher rate of reporting IPV than other ethnic groups, even when other variables are factored in. This thesis adds to the body of evidence on risk factors and strengthens the New Zealand data available, as well as highlighting factors for prevention and areas that policy should be aimed at.</p>

Risk Factors for Intimate Partner Violence Victimisation

<p>Intimate partner violence (IPV) is a significant issue in New Zealand. Reported rates of IPV for New Zealand over a lifetime are estimated at 26% for women 18% for men. Over a 12 month period reported rates of IPV was reported at three percent for women and one point eight percent for men (Morris and Reilly, 2003). This thesis set out to answer the following research questions:1. What factors are strong predictors for intimate partner violence victimisation? 2. Is victim ethnicity factored out when variables such as SES, income and/or employment status are taken into account? This thesis analysed complex survey data from New Zealand National Survey of Crime and Victimisation Survey 2001 (NZNSCV). Logistic regression analysis was use to investigate the risk factors for victimisation reported over a lifetime and reported in the previous 12 month period (2000). Explanatory variables were selected from existing research that identified key variables for analysis. In answer to the research questions: 1. The strong predictors for IPV over a lifetime were Maori, people aged 25-39 years, females, solo parents, over-crowded households, those on socialwelfare benefits, and those divorced orseparated. For 12 month prevalence of IPV strong predictors were those aged 15-24 years, Maori, lower NZSEI groups, solo parents with children, those in defacto relationships and those who do not own or rent a house. 2. Ethnicity is strongly significant in explaining victimisation even when other variables are factored into the model. In particular Maori reported to be twice as likely to be victims of IPV over a lifetime and three-and-a-half times more likely over the past 12 month period (Morris and Riley). This difference in reporting was not as large when other factors where factored in, however Maori remained at a significantly higher rate of reporting IPV than other ethnic groups, even when other variables are factored in. This thesis adds to the body of evidence on risk factors and strengthens the New Zealand data available, as well as highlighting factors for prevention and areas that policy should be aimed at.</p>

Outcomes of multiple gestation births compared to singleton: analysis of multicenter KID database

Background The available data regarding morbidity and mortality associated with multiple gestation births is conflicting and contradicting. Objective To compare morbidity, mortality, and length of stay (LOS) outcomes between multiple gestation (twin, triplet and higher-order) and singleton births. Methods Data from the national multicenter Kids’ Inpatient Database of the Healthcare Cost and Utilization Project from the years 2000, 2003, 2006, 2009, 2012, and 2016 were analyzed using a complex survey design using Statistical Analysis System (SAS) 9.4 (SAS Institute, Cary NC). Neonates with ICD9 and ICD10 codes indicating singletons, twins or triplets, and higher-order multiples were included. Mortality was compared between these groups after excluding transfer outs to avoid duplicate inclusion. To analyze LOS, we included inborn neonates and excluded transfers; who died inpatient and any neonates who appear to have been discharged less than 33 weeks PMA. The LOS was compared by gestational age groups. Results A total of 22,853,125 neonates were analyzed for mortality after applying inclusion-exclusion criteria; 2.96% were twins, and 0.13% were triplets or more. A total of 22,690,082 neonates were analyzed for LOS. Mean GA, expressed as mean (SD), for singleton, twins and triplets, were 38.30 (2.21), 36.39 (4.21), and 32.72 (4.14), respectively. The adjusted odds for mortality were similar for twin births compared to singleton (aOR: 1.004, 95% CI:0.960–1.051, p = 0.8521). The adjusted odds of mortality for triplet or higher-order gestation births were higher (aOR: 1.33, 95% CI: 1.128–1.575, p = 0.0008) when compared to the singleton births. Median LOS (days) was significantly longer in multiple gestation compared to singleton births overall (singletons: 1.59 [1.13, 2.19] vs. twins 3.29 [2.17, 9.59] vs. triplets or higher-order multiples 19.15 [8.80, 36.38], p < .0001), and this difference remained significant within each GA category. Conclusion Multiple gestation births have higher mortality and longer LOS when compared to singleton births. This population data from multiple centers across the country could be useful in counseling parents when caring for multiple gestation pregnancies.

Fitting Pareto Tails to Wealth Survey Data: A Practioners’ Guide

Taking survey data of household wealth as our major example, this short article discusses some of the issues applied researchers are facing when fitting (Type I) Pareto distributions to complex survey data. The contribution of this article is threefold. First, we show how the ordering of the data vector is related to alternative definitions of the empirical CCDF. Second, we provide an intuitive reinterpretation of the bias-corrected estimator developed by Gabaix and Ibragimov (2011), in terms of the alternative definitions of the empirical CCDF, which allows us to generalize their result to the case of complex survey data. Third, we provide computational formulas for standard Kolmogorov-Smirnov (KS) and Cramer-von Mises (CvM) goodness- of-fit tests for complex survey data. Taken together the article provides a concise and hopefully useful presentation of the fundamentals of Pareto tail- fitting with complex survey data.