Dataset Information

Geostatistical analysis and mapping: social and environmental determinants of under-five child mortality, evidence from the 2014 Ghana demographic and health survey.

ABSTRACT:

Background

Under-five mortality (U5M) rates are among the health indicators of utmost importance globally. It is the goal 3 target 2.1 of the Sustainable Development Goals that is expected to be reduced to at least 25 per 1000 livebirths by 2030. Despite a considerable reduction in U5M observed globally, several countries especially those in sub-Saharan Africa (SSA) like Ghana are struggling to meet this target. Evidence-based targeting and utilization of the available limited public health resources are critical for effective design of intervention strategies that will enhance under-five child survival. We aimed to estimate and map U5M risk, with the ultimate goal of identifying communities at high risk where interventions and further research can be targeted.

Methods

The 2014 Ghana Demographic and Health Survey data was used in this study. Geostatistical analyses were conducted on 5884 children residing in 423 geographical clusters. The outcome variable is child survival status (alive or dead). We employed a geostatistical generalised linear mixed model to investigate both measured and unmeasured child specific and spatial risk factors for child survival. We then visualise child mortality by mapping the predictive probability of survival.

Results

Of the total sampled under 5 children, 289 (4.91%) experienced the outcome of interest. Children born as multiple births were at increased risk of mortality with an adjusted odds ratio (aOR) (aOR: 8.2532, 95% CI: [5.2608-12.9477]) compared to singletons. Maternal age increased risk of mortality (aOR: 1.0325, 95% CI: [1.0128-1.0527]). Child's age (aOR: 0.2277, 95% CI: [0.1870-0.2771]) and number of children under 5 within each household (aOR: 0.3166, 95% CI: [0.2614-0.3835]) were shown to have a protective effect. Additionally, mothers with secondary education level (aOR: 0.6258, 95% CI: [0.4298-0.9114]) decreased the risk of U5M. The predicted U5M risk in 2014 was at 5.98%. Substantial residual spatial variations were observed in U5M.

Conclusion

The analysis found that multiple births is highly associated with increased U5M in Ghana. The high-resolution maps show areas and communities where interventions and further research for U5M can be prioritised to have health impact.

SUBMITTER: Aheto JMK

PROVIDER: S-EPMC7501707 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Geostatistical analysis and mapping: social and environmental determinants of under-five child mortality, evidence from the 2014 Ghana demographic and health survey.

Aheto Justice Moses K JMK Yankson Robert R Chipeta Michael Give MG

BMC public health 20200918 1

<h4>Background</h4>Under-five mortality (U5M) rates are among the health indicators of utmost importance globally. It is the goal 3 target 2.1 of the Sustainable Development Goals that is expected to be reduced to at least 25 per 1000 livebirths by 2030. Despite a considerable reduction in U5M observed globally, several countries especially those in sub-Saharan Africa (SSA) like Ghana are struggling to meet this target. Evidence-based targeting and utilization of the available limited public hea ...[more]

PMID: 32948152

Similar Datasets

Project description:BackgroundGlobally, millions of children aged below 5 years die every year and some of these deaths could have been prevented. Though a global problem, under-five mortality is also a major public health problem in Ghana with a rate of 60 deaths per 1000 live births. Identification of drivers of mortality among children aged below 5 years is an important problem that needs to be addressed because it could help inform health policy and intervention strategies aimed at achieving the United Nations SDG Goal 3 target 2. The aim of this study is to develop a predictive model and to identify determinants of under-five mortality.MethodThe 2014 Ghana Demographic and Health Survey data was used in this study. Analyses were conducted on 5884 children. The outcome variable is child survival status (alive or dead). Single level binary logistic and multilevel logistic regression models were employed to investigate determinants of under-five mortality. The fit of the model was checked using Variance Inflation Factor and Likelihood Ratio tests. The Receiver Operating Characteristic curve was used to assess the predictive ability of the models. A p-value< 0.05 was used to declare statistical significance.ResultsThe study observed 289 (4.91%) deaths among children aged below 5 years. The study produced a good predictive model and identified increase in number of total children ever born, number of births in last 5 years, and mothers who did not intend to use contraceptive as critical risk factors that increase the odds of under-five mortality. Also, children who were born multiple and residing in certain geographical regions of Ghana is associated with increased odds of under-five mortality. Maternal education and being a female child decreased the odds of under-five mortality. No significant unobserved household-level variations in under-five mortality were found. The spatial map revealed regional differences in crude under-five mortality rate in the country.ConclusionThis study identified critical risk factors for under-five mortality and strongly highlights the need for family planning, improvement in maternal education and addressing regional disparities in child health which could help inform health policy and intervention strategies aimed at improving child survival.

Project description:BackgroundMorbidity due to Schistosoma haematobium and hookworm infections is marked in those with intense co-infections by these parasites. The development of a spatial predictive decision-support tool is crucial for targeting the delivery of integrated mass drug administration (MDA) to those most in need. We investigated the co-distribution of S. haematobium and hookworm infection, plus the spatial overlap of infection intensity of both parasites, in Ghana. The aim was to produce maps to assist the planning and evaluation of national parasitic disease control programs.Methodology/principal findingsA national cross-sectional school-based parasitological survey was conducted in Ghana in 2008, using standardized sampling and parasitological methods. Bayesian geostatistical models were built, including a multinomial regression model for S. haematobium and hookworm mono- and co-infections and zero-inflated Poisson regression models for S. haematobium and hookworm infection intensity as measured by egg counts in urine and stool respectively. The resulting infection intensity maps were overlaid to determine the extent of geographical overlap of S. haematobium and hookworm infection intensity. In Ghana, prevalence of S. haematobium mono-infection was 14.4%, hookworm mono-infection was 3.2%, and S. haematobium and hookworm co-infection was 0.7%. Distance to water bodies was negatively associated with S. haematobium and hookworm co-infections, hookworm mono-infections and S. haematobium infection intensity. Land surface temperature was positively associated with hookworm mono-infections and S. haematobium infection intensity. While high-risk (prevalence >10-20%) of co-infection was predicted in an area around Lake Volta, co-intensity was predicted to be highest in foci within that area.Conclusions/significanceOur approach, based on the combination of co-infection and co-intensity maps allows the identification of communities at increased risk of severe morbidity and environmental contamination and provides a platform to evaluate progress of control efforts.

Project description:BackgroundMaternal mortality is high in Ghana, averaging 310 maternal deaths per 100,000 live births in 2017. This is partly due to inadequate postnatal care especially among rural communities. Ghana can avert the high maternal deaths if women meet the World Health Organisation's recommended early postnatal care check-up. Despite the association between geographical location and postnatal care utilisation, no study has been done on determinants of postnatal care among rural residents in Ghana. Therefore, this study determined the prevalence and correlates of postnatal care utilization among women in rural Ghana.MethodsThe study utilised women's file of the 2014 Ghana Demographic and Health Survey (GDHS). Following descriptive computation of the prevalence, binary logistic regression was fitted to assess correlates of postnatal care at 95% confidence interval. The results were presented in adjusted odds ratio (AOR). Any AOR less than 1 was interpreted as reduced likelihood of PNC attendance whilst AOR above 1 depicted otherwise. All analyses were done using Stata version 14.0.ResultsThe study revealed that 74% of the rural women had postnatal care. At the inferential level, women residing in Savanna zone had higher odds of postnatal care compared to those in the Coastal zone [AOR = 1.80, CI = 1.023-3.159], just as among the Guan women as compared to the Akan [AOR = 7.15, CI = 1.602-31.935]. Women who were working were more probable to utilise postnatal care compared to those not working [AOR = 1.45, CI = 1.015-2.060]. Those who considered distance as unproblematic were more likely to utilise postnatal care compared to those who considered distance as problematic [AOR = 1.63, CI = 1.239-2.145].ConclusionsThe study showed that ethnicity, ecological zone, occupation and distance to health facility predict postnatal care utilisation among rural residents of Ghana. The study points to the need for government to increase maternal healthcare facilities in rural settings in order to reduce the distance covered by women in seeking postnatal care.

Project description:BackgroundSafe disposal of children's faeces has always been one of the main challenges to good hygiene in Ghana. Although it has been proven that children's faeces are more likely to spread diseases than adults' faeces, people usually mistake them for harmlessness. This study, therefore, sought to determine the prevalence and factors associated with safe disposal of children's faeces in Ghana.MethodsData from the 2014 Ghana Demographic and Health Survey was used for the analysis. A sample size of 2228 mother-child pairs were used for the study. The outcome variable was disposal of children stools. Both bivariate and multivariable logistic regression analyses were performed to identify the factors with safe child stool disposal.ResultsThe prevalence of safe child stool disposal in Ghana was 24.5%. Women in the middle [Adjusted odds ratio (AOR) = 4.62; Confidence Interval (CI) = 3.00-7.10], Coastal Zone [AOR = 4.52; CI = 2.82-7.22], mothers whose children were aged 12-17 [AOR = 1.56; CI = 1.15-2.13] and 18-23 months [AOR = 1.75; CI = 1.29-2.39], and mothers whose household had improved type of toilet facility [AOR = 2.04; CI = 1.53-2.73] had higher odds of practicing safe children's faeces disposal. However, women from households with access to improved source of drinking water [AOR = 0.62; CI = 0.45-2.7] had lower odds of practicing safe children's faeces disposal.ConclusionApproximately only about 25 out of 100 women practice safe disposal of their children's faeces in Ghana. The age of the child, ecological zone, the type of toilet facilities, and the type of drinking water source are associated with the disposal of child faeces. These findings have proven that only improved sanitation (i.e. drinking water and toilet facilities) are not enough for women to safely dispose of their children's faeces. Therefore, in addition to provision of toilet facilities especially in the northern zone of Ghana, there is also the need to motivate and educate mothers on safe disposal of children's stools especially those with children below 12 months. More so, mothers without access to improved toilet facility should also be educated on the appropriate ways to bury their children's stools safely.

Project description:Summary The characteristics and determinants of health and disease are often organized in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial relations. Drawing on statistical parametric mapping, a framework for topological inference well established in the realm of neuroimaging, we propose and validate an approach to the spatial analysis of diverse clinical data—GeoSPM—based on differential geometry and random field theory. We evaluate GeoSPM across an extensive array of synthetic simulations encompassing diverse spatial relationships, sampling, and corruption by noise, and demonstrate its application on large-scale data from UK Biobank. GeoSPM is readily interpretable, can be implemented with ease by non-specialists, enables flexible modeling of complex spatial relations, exhibits robustness to noise and under-sampling, offers principled criteria of statistical significance, and is through computational efficiency readily scalable to large datasets. We provide a complete, open-source software implementation. Highlights • A framework for topological inference applicable to diverse clinical data is proposed• Superior robustness to noise and under-sampling is observed compared with kriging• Application to UK Biobank data is demonstrated The bigger picture Many aspects of health and disease are distributed in space, requiring models of topological organization. The complexity of the task, however, makes spatial analysis comparatively rare in medicine. Here, we introduce GeoSPM, a platform for topological inference from clinical data based on a mature mathematical framework—statistical parametric mapping—validated by decades of use in neuroimaging. We provide comprehensive synthetic evaluation of the approach, and illustrate its application on large-scale data from UK Biobank. The interpretability, flexibility, scalability, ease of implementation, robustness to noise and under-sampling, computational efficiency, and provision of principled criteria of statistical significance, provided by our open-source platform should catalyze wider use of spatial analysis across medicine. We present GeoSPM, an approach to the spatial analysis of diverse clinical data that extends a framework for topological inference, well established in neuroimaging, based on differential geometry and random field theory. We evaluate GeoSPM with extensive synthetic simulations, and apply it to large-scale data from UK Biobank. Our approach is readily interpretable, easy to implement, enables flexible modeling of complex spatial relations, exhibits robustness to noise and under-sampling, offers principled criteria of statistical significance, and is scalable to large datasets.

Dataset Information

Geostatistical analysis and mapping: social and environmental determinants of under-five child mortality, evidence from the 2014 Ghana demographic and health survey.

Background

Methods

Results

Conclusion

Publications

Geostatistical analysis and mapping: social and environmental determinants of under-five child mortality, evidence from the 2014 Ghana demographic and health survey.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets