African stakeholders' views of research options to improve nutritional status in sub-Saharan Africa

Background Setting research priorities for improving nutrition in Africa is currently ad hoc and there is a need to shift the status quo in the light of slow progress in reducing malnutrition. This study explored African stakeholders' views on research priorities in the context of environmental and socio-demographic changes that will impact on nutritional status in Africa in the coming years. Methods Using Multi-Criteria Mapping, quantitative and qualitative data were gathered from 91 stakeholders representing 6 stakeholder groups (health professionals, food Industry, government, civil society, academics and research funders) in Benin, Mozambique, South Africa, Tanzania, Togo and Uganda. Stakeholders appraised six research options (ecological nutrition, nutritional epidemiology, community nutrition interventions, behavioural nutrition, clinical nutrition and molecular nutrition) for how well they could address malnutrition in Africa. Results Impact (28.3%), research efficacy (23.6%) and social acceptability (22.4%) were the criteria chosen the most to evaluate the performance of research options. Research on the effectiveness of community interventions was seen as a priority by stakeholders because they were perceived as likely to have an impact relatively quickly, were inexpensive and cost-effective, involved communities and provided direct evidence of what works. Behavioural nutrition research was also highly appraised. Many stakeholders, particularly academics and government were optimistic about the value of ecological nutrition research (the impact of environmental change on nutritional status). Research funders did not share this enthusiasm. Molecular nutrition was least preferred, considered expensive, slow to have an impact and requiring infrastructure. South Africa ranked clinical and molecular nutrition the highest of all countries. Conclusion Research funders should redirect research funds in Africa towards the priorities identified by giving precedence to develop the evidence for effective community nutrition interventions. Expanding research funding in behavioural and ecological nutrition was also valued and require multi-disciplinary collaborations between nutritionists, social scientists, agricultural and climate change scientists

Community involvement in obstetric emergency management in rural areas: a case of Rukungiri district, Western Uganda

<p>Abstract</p> <p>Background</p> <p>Maternal mortality is a major public health problem worldwide especially in low income countries. Most causes of maternal deaths are due to direct obstetric complications. Maternal mortality ratio remains high in Rukungiri district, western Uganda estimated at 475 per 100,000 live births. The objectives were to identify types of community involvement and examine factors influencing the level of community involvement in the management of obstetric emergencies.</p> <p>Methods</p> <p>We conducted a descriptive study during 2nd to 28th February 2009 in rural Rukungiri district, western Uganda. A total of 448 heads of households, randomly selected from 6/11 (54.5%) of sub-counties, 21/42 (50.0%) parishes and 32/212 (15.1%) villages (clusters), were interviewed. Data were analysed using STATA version 10.0.</p> <p>Results</p> <p>Community pre-emergency support interventions available included community awareness creation (sensitization) while interventions undertaken when emergency had occurred included transportation and referring women to health facility. Community support programmes towards health care (obstetric emergencies) included establishment of community savings and credit schemes, and insurance schemes. The factors associated with community involvement in obstetric emergency management were community members being employed (AOR = 1.91, 95% CI: 1.02 - 3.54) and rating the quality of maternal health care as good (AOR = 2.22, 95% CI: 1.19 - 4.14).</p> <p>Conclusions</p> <p>Types of community involvement in obstetric emergency management include practices and support programmes. Community involvement in obstetric emergency management is influenced by employment status and perceived quality of health care services. Policies to promote community networks and resource mobilization strategies for health care should be implemented. There is need for promotion of community support initiatives including health insurance schemes and self help associations; further community sensitization by empowered community based resource persons rather than health workers and improvement in quality of health care can contribute towards effective management of obstetric complications.</p

Identifying cholera "hotspots" in Uganda: An analysis of cholera surveillance data from 2011 to 2016

<div><p>Background</p><p>Despite advance in science and technology for prevention, detection and treatment of cholera, this infectious disease remains a major public health problem in many countries in sub-Saharan Africa, Uganda inclusive. The aim of this study was to identify cholera hotspots in Uganda to guide the development of a roadmap for prevention, control and elimination of cholera in the country.</p><p>Methodology/Principle findings</p><p>We obtained district level confirmed cholera outbreak data from 2011 to 2016 from the Ministry of Health, Uganda. Population and rainfall data were obtained from the Uganda Bureau of Statistics, and water, sanitation and hygiene data from the Ministry of Water and Environment. A spatial scan test was performed to identify the significantly high risk clusters. Cholera hotspots were defined as districts whose center fell within a significantly high risk cluster or where a significantly high risk cluster was completely superimposed onto a district. A zero-inflated negative binomial regression model was employed to identify the district level risk factors for cholera. In total 11,030 cases of cholera were reported during the 6-year period. 37(33%) of 112 districts reported cholera outbreaks in one of the six years, and 20 (18%) districts experienced cholera at least twice in those years. We identified 22 districts as high risk for cholera, of which 13 were near a border of Democratic Republic of Congo (DRC), while 9 districts were near a border of Kenya. The relative risk of having cholera inside the high-risk districts (hotspots) were 2 to 22 times higher than elsewhere in the country. In total, 7 million people were within cholera hotspots. The negative binomial component of the ZINB model shows people living near a lake or the Nile river were at increased risk for cholera (incidence rate ratio, IRR = 0.98, 95% CI: 0.97 to 0.99, p < .01); people living near the border of DRC/Kenya or higher incidence rate in the neighboring districts were increased risk for cholera in a district (IRR = 0.99, 95% CI: 0.98 to 1.00, p = .02 and IRR = 1.02, 95% CI: 1.01 to 1.03, p < .01, respectively). The zero inflated component of the ZINB model yielded shorter distance to Kenya or DRC border, higher incidence rate in the neighboring districts, and higher annual rainfall in the district were associated with the risk of having cholera in the district.</p><p>Conclusions/significance</p><p>The study identified cholera hotspots during the period 2011–2016. The people located near the international borders, internationally shared lakes and river Nile were at higher risk for cholera outbreaks than elsewhere in the country. Targeting cholera interventions to these locations could prevent and ultimately eliminate cholera in Uganda.</p></div

Descriptive statistics of the study variables by district (n = 112).

<p>Descriptive statistics of the study variables by district (n = 112).</p

Results of the zero-inflated negative binomial model in a multivariable analysis.

<p>Results of the zero-inflated negative binomial model in a multivariable analysis.</p

Distribution of cholera cases by year, 2011–2016, Uganda. No. of affected districts are shown on the top of bar.

<p>Distribution of cholera cases by year, 2011–2016, Uganda. No. of affected districts are shown on the top of bar.</p

Month-wise distribution of the cholera cases and rainfall (mm) by year and by region (eastern vs. western) in Uganda, 2011–2015.

<p>Rainfall data of 2016 were not available, thus the graph of the year was not created.</p