Social disorganization and history of child sexual abuse against girls in sub-Saharan Africa : a multilevel analysis

Background: Child sexual abuse (CSA) is a considerable public health problem. Less focus has been paid to the role of community level factors associated with CSA. The aim of this study was to examine the association between neighbourhood-level measures of social disorganization and CSA. Methods: We applied multiple multilevel logistic regression analysis on Demographic and Health Survey data for 6,351 adolescents from six countries in sub-Saharan Africa between 2006 and 2008. Results: The percentage of adolescents that had experienced CSA ranged from 1.04% to 5.84%. There was a significant variation in the odds of reporting CSA across the communities, suggesting 18% of the variation in CSA could be attributed to community level factors. Respondents currently employed were more likely to have reported CSA than those who were unemployed (odds ratio [OR] = 2.05, 95% confidence interval [CI] 1.48 to 2.83). Respondents from communities with a high family disruption rate were 57% more likely to have reported CSA (OR=1.57, 95% CI 1.14 to 2.16). Conclusion: We found that exposure to CSA was associated with high community level of family disruption, thus suggesting that neighbourhoods may indeed have significant important effects on exposure to CSA. Further studies are needed to explore pathways that connect the individual and neighbourhood levels, that is, means through which deleterious neighbourhood effects are transmitted to individuals

Social factors influencing child health in Ghana

Objectives Social factors have profound effects on health. Children are especially vulnerable to social influences, particularly in their early years. Adverse social exposures in childhood can lead to chronic disorders later in life. Here, we sought to identify and evaluate the impact of social factors on child health in Ghana. As Ghana is unlikely to achieve the Millennium Development Goals’ target of reducing child mortality by two-thirds between 1990 and 2015, we deemed it necessary to identify social determinants that might have contributed to the non-realisation of this goal. Methods ScienceDirect, PubMed, MEDLINE via EBSCO and Google Scholar were searched for published articles reporting on the influence of social factors on child health in Ghana. After screening the 98 articles identified, 34 of them that met our inclusion criteria were selected for qualitative review. Results Major social factors influencing child health in the country include maternal education, rural-urban disparities (place of residence), family income (wealth/poverty) and high dependency (multiparousity). These factors are associated with child mortality, nutritional status of children, completion of immunisation programmes, health-seeking behaviour and hygiene practices. Conclusions Several social factors influence child health outcomes in Ghana. Developing more effective responses to these social determinants would require sustainable efforts from all stakeholders including the Government, healthcare providers and families. We recommend the development of interventions that would support families through direct social support initiatives aimed at alleviating poverty and inequality, and indirect approaches targeted at eliminating the dependence of poor health outcomes on social factors. Importantly, the expansion of quality free education interventions to improve would-be-mother’s health knowledge is emphasised

Associated risk factors of STIs and multiple sexual relationships among youths in Malawi

Having unprotected sex with multiple sexual partners (MSP) is the greatest risk factor for human immunodeficiency virus (HIV) and other sexually transmitted infections (STIs) among youths. Young people with MSPs are less likely to use a condom and the greater the risk for STIs. This study examines the associated risk factors of STIs and multiple sexual partnerships among youths aged 15–24 years. The Malawi Demographic Health Survey 2010 data was used. Out of a sample of 2,987 males and 9,559 females aged 15–24 years, 2,026 males and 6,470 females were considered in the study. Chi square test and logistic regression techniques were performed. Analysis was performed using Statistical Package for Social Sciences (SPSS) version 22.Article writing fellowships were received from the Research Development Office, University of the Western Cape, 7535 Bellville, Cape Town, South Africa.Web of Scienc

Risk factors and a predictive model for under-five mortality in Nigeria: evidence from Nigeria demographic and health survey

<p>Abstract</p> <p>Background</p> <p>Under-5 mortality is a major public health challenge in developing countries. It is essential to identify determinants of under-five mortality (U5M) childhood mortality because these will assist in formulating appropriate health programmes and policies in order to meet the United Nations MDG goal. The objective of this study was to develop a predictive model and identify maternal, child, family and other risk factors associated U5M in Nigeria.</p> <p>Methods</p> <p>Population-based cross-sectional study which explored 2008 demographic and health survey of Nigeria (NDHS) with multivariable logistic regression. Likelihood Ratio Test, Hosmer-Lemeshow Goodness-of-Fit and Variance Inflation Factor were used to check the fit of the model and the predictive power of the model was assessed with Receiver Operating Curve (ROC curve).</p> <p>Results</p> <p>This study yielded an excellent predictive model which revealed that the likelihood of U5M among the children of mothers that had their first marriage at age 20-24 years and ≥ 25 years declined by 20% and 30% respectively compared to children of those that married before the age of 15 years. Also, the following factors reduced odds of U5M: health seeking behaviour, breastfeeding children for > 18 months, use of contraception, small family size, having one wife, low birth order, normal birth weight, child spacing, living in urban areas, and good sanitation.</p> <p>Conclusions</p> <p>This study has revealed that maternal, child, family and other factors were important risk factors of U5M in Nigeria. This study has identified important risk factors that will assist in formulating policies that will improve child survival.</p

Women's sexual health and contraceptive needs after a severe obstetric complication ("near-miss"): a cohort study in Burkina Faso

BACKGROUND: Little is known about the reproductive health of women who survive obstetric complications in poor countries. Our aim was to determine how severe obstetric complications in Burkina Faso affect reproductive events in the first year postpartum. METHODS: Data were collected from a prospective cohort of women who either experienced life threatening (near-miss) pregnancy-related complications or an uncomplicated childbirth, followed from the end of pregnancy to one year postpartum or post-abortum. Documented outcomes include menses resumption, sexual activity resumption, dyspareunia, uptake of contraceptives, unmet needs for contraception and women's reproductive intentions.Participants were recruited in seven hospitals between December 2004 and March 2005 in six towns in Burkina Faso. RESULTS: Reproductive events were associated with pregnancy outcome. The frequency of contraceptive use was low in all groups and the method used varied according to the presence or not of a live baby. The proportion with unmet need for contraception was high and varied according to the time since end of pregnancy. Desire for another pregnancy was highest among near-miss women with perinatal death or natural abortion. Women in the near-miss group with induced abortion, perinatal death and natural abortion had significantly higher odds of subsequent pregnancy. Unintended pregnancies were observed mainly in women in the near-miss group with live birth and the uncomplicated delivery group. CONCLUSIONS: Considering the potential deleterious impact (on health and socio-economic life) of new pregnancies in near-miss women, it is important to ensure family planning coverage includes those who survive a severe complication

Foreground modelling via Gaussian process regression: An application to HERA data

The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in $\sim 2$ hours of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an "intrinsic" and instrumentally corrupted component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating the line of sight power spectrum over scales $k_{\parallel} \le 0.2$ h cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $\sim 1$ MHz (dominating over $k_{\parallel} \sim 0.4 - 0.8$h cMpc$^{-1}$) which should be distinguishable from the 21-cm EoR signal whose typical coherence-scales is $\sim 0.8$ MHz

Foreground modelling via Gaussian process regression: An application to HERA data

The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in $\sim 2$ hours of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an "intrinsic" and instrumentally corrupted component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating the line of sight power spectrum over scales $k_{\parallel} \le 0.2$ h cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $\sim 1$ MHz (dominating over $k_{\parallel} \sim 0.4 - 0.8$h cMpc$^{-1}$) which should be distinguishable from the 21-cm EoR signal whose typical coherence-scales is $\sim 0.8$ MHz

Foreground modelling via Gaussian process regression: an application to HERA data

The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in ∼2 h of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an ‘intrinsic’ and instrumentally corrupted component with a coherence scale of 20 and 2.4 MHz, respectively (dominating the line-of-sight power spectrum over scales k ≤ 0.2 h cMpc−1) and a baseline-dependent periodic signal with a period of ∼1 MHz (dominating over k ∼ 0.4–0.8 h cMpc−1), which should be distinguishable from the 21-cm Epoch of Reionization signal whose typical coherence scale is ∼0.8 MH

Importance of artificial intelligence in evaluating climate change and food safety risk

Climate change is considered primarily as a human-created phenomenon that is changing the way humans live. Nowhere are the impacts of climate change more evident than in the food ecosphere. Climate-induced changes in temperature, precipitation, and rain patterns, as well as extreme weather events have already started impacting the yield, quality, and safety of food. Food safety and the availability of food is a fundamental aspect of ensuring food security and an adequate standard of living. With climate change, there have been increasing instances of observed changes in the safety of food, particularly from a microbiological standpoint, as well as its quality and yield. Thus, there is an urgent need for the implementation of advanced methods to predict the food safety implications of climate change (i.e., future food safety issues) from a holistic perspective (overall food system). Artificial Intelligence (AI) and other such advanced technologies have, over the years, permeated many facets of the food chain, spanning both farm- (or ocean-) to-fork production, and food quality and safety testing and prediction. As a result, these are perfectly positioned to develop novel models to predict future climate change-induced food safety risks. This article provides a roundup of the latest research on the use of AI in the food industry, climate change and its impact on the food industry, as well as the social, ethical, and legal limitations of the same. Particularly, this perspective review stresses the importance of a holistic approach to food safety and quality prediction from a microbiological standpoint, encompassing diverse data streams to help stakeholders make the most informed decisions