Geospatial analysis of childhood morbidity in Ghana

Geospatial disparities and predictors of anaemia among pregnant women in Sub-Saharan Africa

BACKGROUND

Anaemia has become a major public health concern among women in Sub-Saharan Africa (SSA). However, little is known about the spatial disparities in anaemia prevalence and their associated factors among pregnant women in the region. This study analysed the spatial disparities in anaemia and their associated factors among pregnant women in rural and urban settings in SSA.

METHODS

This is a secondary analysis of the most recent demographic and health surveys of 26 countries in SSA. Spatial autocorrelation and hotspot assessment were conducted, while a multivariate logistic regression model was used to identify demographic factors associated with anaemia.

RESULTS

Anaemia was reported among ~50% of pregnant women in urban and rural areas of SSA. The hotspot analysis identified the West African sub-region as having a higher concentration of anaemia cases in rural settings. In urban areas, the odds of anaemia were significantly higher among pregnant women in their second trimester (Adjusted OR = 2.39, CI = 1.99, 2.76). On the other hand, pregnant women in their third trimester (Adjusted OR = 1.98, CI = 1.77, 2.22) and those who had taken intestinal parasite drugs (Adjusted OR = 1.12 CI = 1.02, 1.23) had a higher likelihood of having anaemia in rural areas. Pregnant women aged 35-39 years (Adjusted OR = 0.52, CI = 0.33, 0.81) and those aged 40-44 years (Adjusted OR = 0.69, CI = 0.50, 0.95) had a lesser likelihood of having anaemia compared to women aged 15-19 years in urban and rural areas respectively. Compared to Congo DR, Benin (OR = 2.22, CI = 1.51, 3.28) and Mali (OR = 3.71, CI = 2.73, 5.05) had higher odds of anaemia in urban and rural areas respectively.

CONCLUSIONS

Spatial disparities in anaemia persist among pregnant women in rural and urban settings in SSA. Prevailing spatial variations in anaemia may be addressed by specialised interventions considering the contextual residential settings and socio-economic factors highlighted in this study.

Non-adherence to WHO's recommended 8-contact model: geospatial analysis of the 2017 Maternal Health Survey

INTRODUCTION

Evidence shows that most women in Ghana do not meet the minimum 8-contact model for antenatal care as recommended by WHO with only 31.2%-41.9% of them meeting the recommendation. To the best of our knowledge, no study in Ghana has examined women's noncompliance with the WHO's recommended 8-contact model for antenatal care using geospatial analysis, as this study sets out to do.

METHODS

We sourced data from the recent version of the Ghana Maternal Health Survey which was executed in 2017. A sample of 10,077 women with complete data participated in this study. The link between the explanatory variables and the outcome variable was investigated using binary and multivariate logistic regression models and Spatial analyses such as spatial autocorrelation (Moran's I), hotspot, cluster and outlier analysis, and geographically weighted regression were conducted using ArcMap version 10.7.

RESULTS

Districts found in the north-eastern and south-western parts of the country were more likely to experience noncompliance with ANC. Women staying within the middle belt without health insurance were more likely (17-29%) to be noncompliant with ANC. Women with low community socioeconomic status were found to be more likely (17-34%) to be noncompliant with ANC in the eastern parts of Ghana.

CONCLUSION

The study has shown that in order to achieve targets one and three of Sustainable Development Goal 3, the government of Ghana, the Ministry of Health, together with the Ghana Health Service may have to intensify health education in the identified areas to highlight the importance of adherence to the WHO recommendations on ANC 8-contact model.

High-risk fertility behaviours among women of reproductive ages in the Democratic Republic of the Congo: Prevalence, correlates, and spatial distribution

BACKGROUND

High-risk fertility behaviour remains a major public health in the Democratic Republic of the Congo, like other sub-Saharan Africa countries, especially because Total Fertility Rate (TFR) is very high in the country, estimated at 6.6 children. Despite the commendable progress in reducing maternal deaths in the region, sub-Saharan Africa is still lagging very behind compared with other regions. Yet, high-risk fertility behaviours are pivotal to improve maternal and child health. This study aims to assess geographical variations of, and to identify risk factors associated with high-risk fertility behaviours among married women in the Democratic Republic of the Congo using the 2013-14 Demographic and Health Survey.

METHODS

Overall, 11,497 married women were selected from a nationally representative using a two-stage sampling design. Standard logistic regressions were performed to identify individual- and household/community-level factors associated with high-risk fertility behaviours. Additionally, interactions between women's age and education, and urban residence were tested. Bernoulli based spatial scan statistics were used to identify the presence of high-risk fertility behaviours spatial clusters using Kulldorff's SaTScan version 9.6 software. ArcGIS 10.7 was used to visualize the spatial variations of high-risk fertility behaviours. Geographically weighted regression (GWR) analysis was employed using Multiscale GWR version 2.0 software.

RESULTS

Findings indicated that more than two-third of married women exhibited high-risk fertility behaviours in the Democratic Republic of the Congo. Multivariate logistic regression showed that education was negatively and significantly associated with the odds of high-risk fertility behaviours. In contrast, women's age significantly increased the odds of high-risk fertility behaviours. Interactions between urban residence and women's education and age confirmed the urban advantage identified from previous studies. Finally, high-risk fertility behaviours were highly clustered in the Northeastern provinces of the country.

CONCLUSION

The study showed that there were significant geographical variations of high-risk fertility behaviours across provinces in the Democratic Republic of the Congo. The paper also identified significant-high hot spots of high-risk fertility behaviours in the Northeastern provinces of the country. To reduce high-risk fertility behaviours, and ultimately improve maternal and child outcomes in the country, policymakers and health planners need to strategically address these inequalities. Finally, this paper highlighted the persistent needs of country-specific studies due to differences across sub-Saharan African countries in terms of social development and cultures.

Exploring the effect of the primary care health workers number on infectious diarrhea morbidity and where the health resources should go

We aimed to explore the association between the number of primary healthcare workers and infectious diarrhea morbidity at community levels and to provide evidence-based implications for optimizing primary healthcare manpower resource allocations. We collected annual infectious diarrhea morbidity and relevant data of 4321 communities in Sichuan Province, China, from 2017 to 2019. Global and local Moran’s I were calculated to detect the spatial clustering of infectious diarrhea morbidity and to identify areas where increased primary healthcare manpower resources should be allocated. The spatial lag fixed effects panel data model was adopted to explore the association between the number of primary healthcare workers per 1000 residents and infectious diarrhea morbidity. Significantly high–high and low–low clusters of infectious diarrhea cases were found to be mainly distributed in underdeveloped and developed areas during the studied period years, respectively. The infectious diarrhea morbidity was found to be statistically negatively associated with the number of primary healthcare workers per 1000 residents with a coefficient of − 0.172, indicating that a 0.172 reduction of infectious diarrhea morbidity (1/10,000) was associated with doubled amounts of primary healthcare workers per 1000 residents. Our findings highlighted the role of primary healthcare in the process of infectious diarrhea prevention and control, and implied that constant efforts should be addressed to facilitate infectious diarrhea prevention and control, especially in the underdeveloped areas.

Gut microbiota signature of pathogen-dependent dysbiosis in viral gastroenteritis

Acute gastroenteritis associated with diarrhea is considered a serious disease in Africa and South Asia. In this study, we examined the trends in the causative pathogens of diarrhea and the corresponding gut microbiota in Ghana using microbiome analysis performed on diarrheic stools via 16S rRNA sequencing. In total, 80 patients with diarrhea and 34 healthy adults as controls, from 2017 to 2018, were enrolled in the study. Among the patients with diarrhea, 39 were norovirus-positive and 18 were rotavirus-positive. The analysis of species richness (Chao1) was lower in patients with diarrhea than that in controls. Beta-diversity analysis revealed significant differences between the two groups. Several diarrhea-related pathogens (e.g., Escherichia-Shigella, Klebsiella and Campylobacter) were detected in patients with diarrhea. Furthermore, co-infection with these pathogens and enteroviruses (e.g., norovirus and rotavirus) was observed in several cases. Levels of both Erysipelotrichaceae and Staphylococcaceae family markedly differed between norovirus-positive and -negative diarrheic stools, and the 10 predicted metabolic pathways, including the carbohydrate metabolism pathway, showed significant differences between rotavirus-positive patients with diarrhea and controls. This comparative study of diarrheal pathogens in Ghana revealed specific trends in the gut microbiota signature associated with diarrhea and that pathogen-dependent dysbiosis occurred in viral gastroenteritis.

Multilevel geospatial analysis of factors associated with unskilled birth attendance in Ghana

Background

Globally, about 810 women die every day due to pregnancy and its related complications. Although the death of women during pregnancy or childbirth has declined from 342 deaths to 211 deaths per 100,000 live births between 2000 and 2017, maternal mortality is still higher, particularly in sub-Saharan Africa and South Asia, where 86% of all deaths occur.

Methods

A secondary analysis was carried out using the 2014 Ghana Demographic and Health Survey. A sample total of 4,290 women who had a live birth in the 5 years preceding the survey was included in the analysis. GIS software was used to explore the spatial distribution of unskilled birth attendance in Ghana. The Geographic Weighted Regression (GWR) was employed to model the spatial relationship of some predictor of unskilled birth attendance. Moreover, a multilevel binary logistic regression model was fitted to identify factors associated with unskilled birth attendance.

Results

In this study, unskilled birth attendance had spatial variations across the country. The hotspot, cluster and outlier analysis identified the concerned districts in the north-eastern part of Ghana. The GWR analysis identified different predictors of unskilled birth attendance across districts of Ghana. In the multilevel analysis, mothers with no education, no health insurance coverage, and mothers from households with lower wealth status had higher odds of unskilled birth attendance. Being multi and grand multiparous, perception of distance from the health facility as not a big problem, urban residence, women residing in communities with medium and higher poverty level had lower odds of unskilled birth attendance.

Conclusion

Unskilled birth attendance had spatial variations across the country. Areas with high levels of unskilled birth attendance had mothers who had no formal education, not health insured, mothers from poor households and communities, primiparous women, mothers from remote and border districts could get special attention in terms of allocation of resources including skilled human power, and improved access to health facilities.

Spatial patterns of lower respiratory tract infections and their association with fine particulate matter

This study aimed to identify the spatial patterns of lower respiratory tract infections (LRIs) and their association with fine particulate matter (PM_2.5). The disability-adjusted life year (DALY) database was used to represent the burden each country experiences as a result of LRIs. PM_2.5 data obtained from the Atmosphere Composition Analysis Group was assessed as the source for main exposure. Global Moran's I and Getis-Ord Gi* were applied to identify the spatial patterns and for hotspots analysis of LRIs. A generalized linear mixed model was coupled with a sensitivity test after controlling for covariates to estimate the association between LRIs and PM_2.5. Subgroup analyses were performed to determine whether LRIs and PM_2.5 are correlated for various ages and geographic regions. A significant spatial auto-correlated pattern was identified for global LRIs with Moran's Index 0.79, and the hotspots of LRIs were clustered in 35 African and 4 Eastern Mediterranean countries. A consistent significant positive association between LRIs and PM_2.5 with a coefficient of 0.21 (95% CI 0.06-0.36) was identified. Furthermore, subgroup analysis revealed a significant effect of PM_2.5 on LRI for children (0-14 years) and the elderly (≥ 70 years), and this effect was confirmed to be significant in all regions except for those comprised of Eastern Mediterranean countries.