Seroprevalence of maternal HIV, hepatitis B, and syphilis in a major maternity hospital in North Kordofan, Sudan.

Routine infectious diseases screening of Sudanese pregnant women has been patchy due to scarcity of healthcare resources and social stigma. We sought to determine the seroprevalence of HIV, hepatitis B, and syphilis among pregnant women attending antenatal care (ANC) at El Obeid Maternity Hospital in western Sudan. We also explored the association between these infections and a set of socio-demographic and maternal variables. Unlinked anonymous testing for HIV-1/2 antibodies, hepatitis B surface antigen, and Treponema pallidum antibodies was performed on residual blood samples collected during routine ANC (August 2016-March 2017). Seroprevalence of HIV was 1.13% (5/444; 95% CI 0.37-2.61%), hepatitis B 2.93% (13/444; 95% CI 1.57-4.95%), and syphilis 7.43% (33/444; 95% CI 5.17-10.28%). On bivariate analysis, there were no statistically significant associations between hepatitis B, syphilis, or a composite outcome including any of the three infections and age, stage of pregnancy, gravidity, parity, previous mode of delivery, history of blood transfusion, or husband polygamy. Urgent action is needed to scale up routine maternal screening for HIV, hepatitis B, and syphilis on an opt-out basis. Further research into the socio-demographic and behavioural determinants of these infections as well as their clinical outcomes is needed.

Statistical Methods for Predicting Malaria Incidences Using Data from Sudan.

Malaria is the leading cause of illness and death in Sudan. The entire population is at risk of malaria epidemics with a very high burden on government and population. The usefulness of forecasting methods in predicting the number of future incidences is needed to motivate the development of a system that can predict future incidences. The objective of this paper is to develop applicable and understood time series models and to find out what method can provide better performance to predict future incidences level. We used monthly incidence data collected from five states in Sudan with unstable malaria transmission. We test four methods of the forecast: (1) autoregressive integrated moving average (ARIMA); (2) exponential smoothing; (3) transformation model; and (4) moving average. The result showed that transformation method performed significantly better than the other methods for Gadaref, Gazira, North Kordofan, and Northern, while the moving average model performed significantly better for Khartoum. Future research should combine a number of different and dissimilar methods of time series to improve forecast accuracy with the ultimate aim of developing a simple and useful model for producing reasonably reliable forecasts of the malaria incidence in the study area.