Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana. The authors sought to develop a statistical model for neonatal mortality in Ghana and its risk factors. They reviewed the birth history data from 5884 children born in the 5 years preceding the 2014 Ghana Demographic and Health Survey (GDHS), and found a neonatal mortality prevalence of 2.8%. They found an increased risk of neonatal death among babies born to mothers who received prenatal care from non-skilled worker [OR: 3.79 (95% CI: 2.52, 5.72)], multiple births [OR: 3.10 (95% CI: 1.89, 15.27)], babies delivered through caesarian section [OR: 2.24 (95% CI: 1.30, 3.85)], and household with 1 to 4 members [OR: 5.74 (95% CI: 3.16, 10.43)], respectively. The predictive accuracy of the unweighted penalized and weighted single-level multivariable logistic regression models was 82% and 80%, respectively. The study advocates that prudent and holistic interventions should be institutionalized and implemented to address the risk factors identified in order to reduce neonatal death and, by large, improve child and maternal health outcomes to achieve the SDG target 3.2.
Effects of long-term Doppler ultrasound exposure on cochlea and cochlear nucleus in prenatal period in an experimental model. The authors used a rat model to investigate the effects of new generation Doppler ultrasonography (DUSG) application at different frequencies in prenatal period on cochlea and cochlear nucleus. They studied pregnant female rats subdivided into controls, those subjected to ultrasound every other day from postnatal day 4-18 (20 min/15 per day), and a third group treated daily on these days. There were no histopathological differences, changes in caspase3 expression, or in distortion-product otoacoustic emissions in the 3 groups. Thus, multiple administration of new generation DUSG to pregnant rats did not cause any harm in the cochlear neural tissue.
Modeling the effect of lockdown timing as a COVID-19 control measure in countries with differing social contacts. The application, timing, and duration of lockdown strategies during a pandemic remain poorly quantified for expected public health outcomes. Previous projection models show conflicting conclusions about the impact of lockdowns on COVID-19 outcomes. The authors developed a stochastic continuous-time Markov chain (CTMC) model. The analysis for the effect of a lockdown was performed without the influence of the other control measures, like social distancing and mask wearing, to quantify its absolute effect. Hypothetical lockdown timing was shown to be the critical parameter in ameliorating pandemic peak incidence. They also found well-timed lockdowns could split the peak of hospitalizations into two smaller distant peaks while extending the pandemic duration. The timing of lockdowns revealed that a "tunneling" effect on incidence can be achieved to bypass the peak and prevent pandemic caseloads from exceeding hospital capacity.