Assessment of Neonatal Mortality and Associated Hospital-Related Factors in Healthcare Facilities Within Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana.

Objectives: Ghana's quest to reduce neonatal mortality, in hospital facilities and communities, continues to be a nightmare. The pursuit of achieving healthy lives and well-being for neonates as enshrined in Sustainable Development Goal three lingered in challenging hospital facilities and communities. Notwithstanding that, there have been increasing efforts in that direction. This study examines the contributing factors that hinder the fight against neonatal mortality in all hospital facilities in the Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana. Methods: The study utilized neonatal mortality data consisting of neonatal deaths, structural facility related variables, medical human resources, types of hospital facilities and natal care. The data was collected longitudinally from 2014 to 2019. These variables were analysed using the negative binomial hurdle regression (NBH) model to determine factors that contribute to this menace at the facility level. Cause-specific deaths were obtained to determine the leading causes of neonatal deaths within health facilities in the two municipal assemblies. Results: The study established that the leading causes of neonatal mortality in these districts are birth asphyxia (46%), premature birth (33%), neonatal sepsis (11%) and neonatal jaundice (7%). The NBH showed that neonatal mortality in hospital facilities depend on the number of incubators, monitoring equipment, hand washing facilities, CPAPb machines, radiant warmers, physiotherapy machines, midwives, paediatric doctors and paediatric nurses in the hospital facility. Conclusions: Early management of neonatal sepsis, birth asphyxia, premature birth and neonatal infections is required to reduce neonatal deaths. The government and all stakeholders in the health sector should provide all hospital facilities with the essential equipment and the medical human resources necessary to eradicate the menace. This will make the realization of Sustainable Development Goal three, which calls for healthy lives and well-being for all, a reality.

The relationship between anaemia and the use of treated bed nets among pregnant and non-pregnant women in Ghana.

Studies have indicated that the risk of malaria, particularly its association with anaemia in pregnant women, increases when treated bed nets are not used. This paper utilizes a statistical mechanical model to investigate whether there is a statistical relationship between the presence or absence of anaemia in pregnant and non-pregnant women and their decision to sleep under treated bed nets. Data from the Ghana Malaria Indicator Survey (GMIS), which includes both rural and urban malaria-endemic areas in Ghana, were employed in this study. A total of 2,434 women, comprising 215 pregnant and 2,219 non-pregnant participants, were involved. Among these, 4.76% of the pregnant and anaemic women and 45.89% of the non-pregnant and anaemic women slept under treated bed nets, while 0.86% of the pregnant and anaemic and 6.82% of the non-pregnant and anaemic women did not. The findings revealed that, in the absence of social interaction, non-anaemic pregnant women have a lower prevalence of choosing to use bed nets compared to their anaemic counterparts. Additionally, non-pregnant anaemic women showed a positive private incentive (30.87%) to use treated bed nets, implying a positive correlation between anaemia and the choice to sleep in a treated bed net. Furthermore, the study demonstrated that both pregnancy and anaemia status have a relationship with the use of treated bed nets in Ghana, especially when social interactions are considered. The interaction strength between non-pregnant and anaemic women interacting with each other shows a negative estimate (-1.49%), implying that there is no rewarding effect from imitation. These insights are crucial for malaria prevention and control programs, emphasizing the need for targeted interventions to enhance the use of treated bed nets among both pregnant and non-pregnant women in Ghana's malaria-endemic regions.

A hybrid forecasting technique for infection and death from the mpox virus.

Objectives: The rising of new cases and death counts from the mpox virus (MPV) is alarming. In order to mitigate the impact of the MPV it is essential to have information of the virus's future position using more precise time series and stochastic models. In this present study, a hybrid forecasting system has been developed for new cases and death counts for MPV infection using the world daily cumulative confirmed and death series. Methods: The original cumulative series was decomposed into new two subseries, such as a trend component and a stochastic series using the Hodrick-Prescott filter. To assess the efficacy of the proposed models, a comparative analysis with several widely recognized benchmark models, including auto-regressive (AR) model, auto-regressive moving average (ARMA) model, non-parametric auto-regressive (NPAR) model and artificial neural network (ANN), was performed. Results: The introduction of two novel hybrid models, HPF11 and HPF34, which demonstrated superior performance compared to all other models, as evidenced by their remarkable results in key performance indicators such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), is a significant advancement in disease prediction. Conclusion: The new models developed can be implemented in forecasting other diseases in the future. To address the current situation effectively, governments and stakeholders must implement significant changes to ensure strict adherence to standard operating procedures (SOPs) by the public. Given the anticipated continuation of increasing trends in the coming days, these measures are essential for mitigating the impact of the outbreak.