Epidemiology of emergency and elective caesarean section and its association with early neonatal mortality in sub-Saharan African countries

INTRODUCTION

Investigating elective and emergency caesarean section (CS) separately is important for a better understanding of birth delivery modes in the sub-Saharan Africa (SSA) region and identifying bottlenecks that prevent favourable childbirth outcomes in SSA. This study aimed at evaluating the prevalences of both CS types, determining their associated socioeconomic factors and their association with early neonatal mortality in SSA.

METHODS

SSA countries Demographic and Health Surveys data that had collected information on the CS' timing were included in our study. A total of 21 countries were included in this study, with a total of 155 172 institutional live births. Prevalences of both CS types were estimated at the countries' level using household sampling weights. Multilevel models were fitted to identify associated socioeconomic factors of both CS types and their associations with early neonatal mortality.

RESULTS

The emergency CS prevalence in SSA countries was estimated at 4.6% (95% CI 4.4-4.7) and was higher than the elective CS prevalence estimated at 3.4% (95% CI 3.3-3.6). Private health facilities' elective CS prevalence was estimated at 10.2% (95% CI 9.3-11.2) which was higher than the emergency CS prevalence estimated at 7.7% (95% CI 7.0-8.5). Conversely, in public health facilities, the emergency CS prevalence was estimated at 4.0% (95% CI 3.8-4.2) was higher than the elective CS prevalence estimated at 2.7% (95% CI 2.6-2.8). The richest women were more likely to have birth delivery by both CS types than normal vaginal delivery. Emergency CS was positively associated with early neonatal mortality (adjusted OR=2.37, 95% CI 1.64-3.41), while no association was found with elective CS.

CONCLUSIONS

Findings suggest shortcomings in pregnancy monitoring, delivery preparation and postnatal care. Beyond antenatal care (ANC) coverage, more attention should be put on quality of ANC, postnatal care, emergency obstetric and newborn care for favourable birth delivery outcomes in SSA.

Hot-spots of HIV infection in Cameroon: a spatial analysis based on Demographic and Health Surveys data

Background

The Human Immunodeficiency Virus(HIV) infection prevalence in Cameroon has decreased from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.28\%$$\end{document}5.28% in 2004 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.8\%$$\end{document}2.8% in 2018. However, this decrease in prevalence does not show disparities especially in terms of spatial or geographical pattern. Efficient control and fight against HIV infection may require targeting hotspot areas. This study aims at presenting a cartography of HIV infection situation in Cameroon using the 2004, 2011 and 2018 Demographic and Health Survey data, and investigating whether there exist spatial patterns of the disease, may help to detect hot-spots.

Methods

HIV biomarkers data and Global Positioning System (GPS) location data were obtained from the Cameroon 2004, 2011, and 2018 Demographic and Health Survey (DHS) after an approved request from the MEASURES Demographic and Health Survey Program. HIV prevalence was estimated for each sampled area. The Moran’s I (MI) test was used to assess spatial autocorrelation. Spatial interpolation was further performed to estimate the prevalence in all surface points. Hot-spots were identified based on Getis–Ord (Gi*) spatial statistics. Data analyses were done in the R software(version 4.1.2), while Arcgis Pro software tools’ were used for all spatial analyses.

Results

Generally, spatial autocorrelation of HIV infection in Cameroon was observed across the three time periods of 2004 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MI=0.84$$\end{document}MI=0.84, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p-value < 0.001$$\end{document}p-value<0.001), 2011 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MI=0.80$$\end{document}MI=0.80, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p-value < 0.001$$\end{document}p-value<0.001) and 2018 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MI=0.87$$\end{document}MI=0.87, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p-value < 0.001$$\end{document}p-value<0.001). Subdivisions in which one could find persistent hot-spots for at least two periods including the last period 2018 included: Mbéré, Lom et Djerem, Kadey, Boumba et Ngoko, Haute Sanaga, Nyong et Mfoumou, Nyong et So’o Haut Nyong, Dja et Lobo, Mvila, Vallée du Ntem, Océan, Nyong et Kellé, Sanaga Maritime, Menchum, Dounga Mantung, Boyo, Mezam and Momo. However, Faro et Déo emerged only in 2018 as a subdivision with HIV infection hot-spots.

Conclusion

Despite the decrease in HIV epidemiology in Cameroon, this study has shown that there are spatial patterns for HIV infection in Cameroon and possible hot-spots have been identified. In its effort to eliminate HIV infection by 2030 in Cameroon, the public health policies may consider these detected HIV hot-spots, while maintaining effective control in other parts of the country.