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Abstract Background Half of global child deaths occur in sub-Saharan Africa. Understanding child mortality patterns and risk factors will help inform interventions to reduce this heavy toll. The Nanoro Health and Demographic Surveillance System (HDSS), Burkina Faso was described previously, but patterns and potential drivers of heterogeneity in child mortality in the district had not been studied. Similar studies in other districts indicated proximity to health facilities as a risk factor, usually without distinction between facility types. Methods Using Nanoro HDSS data from 2009 to 2013, we estimated the association between under-5 mortality and proximity to inpatient and outpatient health facilities, seasonality of death, age group, and standard demographic risk factors. Results Living in homes 40–60 min and > 60 min travel time from an inpatient facility was associated with 1.52 (95% CI: 1.13–2.06) and 1.74 (95% CI: 1.27–2.40) greater hazard of under-5 mortality, respectively, than living in homes

Abstract Background Achieving food security remains a key challenge for public policy throughout the world. As such, understanding the determinants of food insecurity and the causal relationships between them is an important scientific question. We aim to construct a Bayesian belief network model of food security in rural South Africa to act as a tool for decision support in the design of interventions. Methods Here, we use data from the Agincourt Health and Socio-demographic Surveillance System (HDSS) study area, which is close to the Mozambique border in a low-income region of South Africa, together with Bayesian belief network (BBN) methodology to address this question. Results We find that a combination of expert elicitation and learning from data produces the most credible set of causal relationships, as well as the greatest predictive performance with 10-fold cross validation resulting in a Briers score 0.0846, information reward of 0.5590, and Bayesian information reward of 0.0057. We report the resulting model as a directed acyclic graph (DAG) that can be used to model the expected effects of complex interventions to improve food security. Applications to sensitivity analyses and interventional simulations show ways the model can be applied as tool for decision support for human experts in deciding on interventions. Conclusions The resulting models can form the basis of the iterative generation of a robust causal model of household food security in the Agincourt HDSS study area and in other similar populations.

Background: Half of global child deaths occur in sub-Saharan Africa. Understanding child mortality patterns and risk factors will help inform interventions to reduce this heavy toll. The Nanoro Health and Demographic Surveillance System (HDSS), Burkina Faso was described previously, but patterns and potential drivers of heterogeneity in child mortality in the district had not been studied. Similar studies in other districts indicated proximity to health facilities as a risk factor, usually without distinction between facility types.Methods: Using Nanoro HDSS data from 2009 to 2013, we estimated the association between under-5 mortality and proximity to inpatient and outpatient health facilities, seasonality of death, age group, and standard demographic risk factors.Results: Living in homes 40-60 min and > 60 min travel time from an inpatient facility was associated with 1.52 (95% CI: 1.13-2.06) and 1.74 (95% CI: 1.27-2.40) greater hazard of under-5 mortality, respectively, than living in homes < 20 min from an inpatient facility. No such association was found for outpatient facilities. The wet season (July-November) was associated with 1.28 (95% CI: 1.07, 1.53) higher under-5 mortality than the dry season (December-June), likely reflecting the malaria season.Conclusions: Our results emphasize the importance of geographical proximity to health care, distinguish between inpatient and outpatient facilities, and also show a seasonal effect, probably driven by malaria. [ABSTRACT FROM AUTHOR]

Background: Achieving food security remains a key challenge for public policy throughout the world. As such, understanding the determinants of food insecurity and the causal relationships between them is an important scientific question. We aim to construct a Bayesian belief network model of food security in rural South Africa to act as a tool for decision support in the design of interventions.Methods: Here, we use data from the Agincourt Health and Socio-demographic Surveillance System (HDSS) study area, which is close to the Mozambique border in a low-income region of South Africa, together with Bayesian belief network (BBN) methodology to address this question.Results: We find that a combination of expert elicitation and learning from data produces the most credible set of causal relationships, as well as the greatest predictive performance with 10-fold cross validation resulting in a Briers score 0.0846, information reward of 0.5590, and Bayesian information reward of 0.0057. We report the resulting model as a directed acyclic graph (DAG) that can be used to model the expected effects of complex interventions to improve food security. Applications to sensitivity analyses and interventional simulations show ways the model can be applied as tool for decision support for human experts in deciding on interventions.Conclusions: The resulting models can form the basis of the iterative generation of a robust causal model of household food security in the Agincourt HDSS study area and in other similar populations. [ABSTRACT FROM AUTHOR]

Abstract Background Despite the high burden of disability in Ethiopia, little is known about it, particularly in the study area. Hence, this study aimed to investigate the prevalence and factors associated with disability at Dabat Health and Demographic Surveillance System (HDSS) site, northwest Ethiopia. Method A population-based study was conducted from October to December 2014 at Dabat HDSS site. A total of 67,395 people were included in the study. The multivariable binary logistic regression analysis was employed to identify factors associated with disability. The Adjusted Odds Ratio (AOR) with a 95% Confidence Interval (CI) was estimated to show the strength of association. A p-value of

Background: Malaria is the leading cause of disease burden across the world, especially in African countries. Ethiopia has designed a five year (2011-2015) plan to cover 100% of the households in malarious areas with one insecticide treated net (ITN) for every two persons, and to raise consistent ITN utilization to at least 80%. However, evidence on ownership of ITN among malarious rural households in northwest Ethiopia is quite limited. Hence, the present study aimed at assessing ownership of ITN and associated factors among rural households at risk of malaria at Dabat Health and Demographic Surveillance System site, northwest Ethiopia.Methods: A cross sectional re-census was carried out in Dabat Health and Demographic Surveillance System site during peak malaria seasons from October to December, 2014. Data for 15,088 households at Dabat Health and Demographic Surveillance System site were used for the analysis. Descriptive measures and binary logistic regression were carried out.Results: Among those who owned at least one ITN, 53.4% were living at an altitude >2500 m above sea level. However, out of households living at an altitude <2000 m above sea level, 15.8% (95% CI 14.4%, 17.3%) owned ITN at an average of 4.3 ± 2.1 persons per ITN. Of these, 69.5% (95% CI 64.7%, 74.1%) used the ITN. Among utilizing households at malarious areas, 23.7% prioritized pregnant women and 31.4% children to use ITN. The availability of radio receiver/mobile (AOR 1.60, 95%CI 1.08, 2.35) and secondary/above educational status of household member (AOR 1.54, 95%CI 1.19, 2.04) were predictors of ownership of ITN.Conclusion: Rural households at risk of malaria did not own a sufficient number of ITN though the utilization is promising. Moreover, prioritizing children and pregnant women to sleep under ITN remains public health problems. Programmers, partners and implementers should consider tailored intervention strategy stratified by altitude in distributing ITN. ITN distribution should also be accompanied by using exhaustive promotion strategies that consider people without access to any source of information, and educating households to prioritize pregnant and under five children to sleep under ITN. [ABSTRACT FROM AUTHOR]

Background: Despite the high burden of disability in Ethiopia, little is known about it, particularly in the study area. Hence, this study aimed to investigate the prevalence and factors associated with disability at Dabat Health and Demographic Surveillance System (HDSS) site, northwest Ethiopia.Method: A population-based study was conducted from October to December 2014 at Dabat HDSS site. A total of 67,395 people were included in the study. The multivariable binary logistic regression analysis was employed to identify factors associated with disability. The Adjusted Odds Ratio (AOR) with a 95% Confidence Interval (CI) was estimated to show the strength of association. A p-value of <0.05 was used to declare statistical significance.Results: One thousand two hundred twenty-eight individuals were reported to have a disability giving a prevalence rate of 1.82%, of which, about 39% was related to a vision disability. The high odds of disability were observed among the elderly (≥50 years) [AOR: 4.49; 95% CI: 1.95, 10.33], severely food in-secured [AOR: 2.11; 95% CI: 1.59, 2.80], and separated marital status [AOR: 7.52; 95% CI: 1.18, 47.84]. While having a paid job [AOR: 0.46; 95% CI: 0.28, 0.77], being in the richest quintile [AOR: 0.55; 95% CI: 0.41, 0.75], and high engagement in work-related physical activities [AOR: 0.36; 95% CI: 0.27, 0.49] were inversely associated with the disability.Conclusion: Disability is a major public health problem, and the burden is noticeable in the study area. Vision disability is the highest of all disabilities. Thus, efforts must be made on educating the public about disability and injury prevention. Measures that reduce disability should target the elderly, the poorer and the unemployed segment of the population. [ABSTRACT FROM AUTHOR]