Under-five child mortality declined 47% since 2000 following the implementation of the United Nation’s (UN) Millennium Development Goals. To further reduce under-five child mortality, the UN’s Sustainable Development Goals (SDGs) will focus on interventions to address neonatal mortality, a major contributor of under-five mortality. The African region has the highest neonatal mortality rate (28.0 per 1000 live births), followed by that of the Eastern Mediterranean (26.6) and South-East Asia (24.3). This study used the Demographic and Health Survey Birth Recode data (http://dhsprogram.com/data/File-Types-and-Names.cfm) to identify high-risk districts and countries for neonatal mortality in two sub-regions of Africa – East Africa and West Africa. Geographically weighted Poisson regression models were estimated to capture the spatially varying relationships between neonatal mortality and dimensions of potential need i) <em>care around the time of delivery</em>, ii) <em>maternal education</em>, and iii) <em>women’s empowerment</em>. In East Africa, neonatal mortality was significantly associated with home births, mothers without an education and mothers whose husbands decided on contraceptive practices, controlling for rural residency. In West Africa, neonatal mortality was also significantly associated with home births, mothers with a primary education and mothers who did not want or plan their last child. Importantly, neonatal mortality associated with home deliveries were explained by maternal exposure to unprotected water sources in East Africa and older maternal age and female sex of infants in West Africa. Future SDG-interventions may target these dimensions of need in priority high-risk districts and countries, to further reduce the burden of neonatal mortality in Africa.

As countries of sub-Saharan Africa expand irrigation to improve food security and foster economic growth, it is important to quantify the malaria risk associated with this process. Irrigated ecosystems can be associated with increased malaria risk, but this relationship is not fully understood. We studied this relationship at the Bwanje Valley Irrigation Scheme (800 hectares) in Malawi. Household prevalence of malaria and indoor Anopheles density were quantified in two cross-sectional studies in 2016 and 2017 (5,829 residents of 1,091 households). Multilevel logistic regression was used to estimate the association between distance to the irrigation scheme and malaria infection and mosquito density. The prevalence of malaria infection was 50.2% (2,765/5,511) by histidine-rich protein 2–based malaria rapid diagnostic tests and 30.1% (1,626/5,403) by microscopy. Individuals residing in households within 3 km of the scheme had significantly higher prevalence of infection (adjusted odds ratio [aOR] = 1.41; 95% confidence interval [CI] 1.18, 1.68); school-aged children had the highest prevalence among age groups (aOR = 1.34; 95% CI 1.11, 1.63). Individuals who reported bed net use, and households with higher socioeconomic status and higher level of education for household head or spouse, had lower odds of malaria infection. Female Anopheles mosquitoes (2,215 total; Anopheles arabiensis, 90.5%, Anopheles funestus, 9.5%) were significantly more abundant in houses located within 1.5 km of the scheme. Proximity of human dwellings to the irrigation scheme increased malaria risk, but higher household wealth index reduced risk. Therefore, multisectoral approaches that spur economic growth while mitigating increased malaria transmission are needed for people living close to irrigated sites.

Background: The association between irrigation and the proliferation of adult mosquitoes including malaria vectors is well known; however, irrigation schemes are treated as homogenous spatio-temporal units, with little consideration for how larval breeding varies across space and time. The objective of this study was to estimate the spatio-temporal distribution of pools of water facilitating breeding at the Bwanje Valley Irrigation Scheme (BVIS) in Malawi, Africa as a function of environmental and anthropogenic characteristics. Methods: Irrigation structure and land cover were quantified during the dry and rainy seasons of 2016 and 2017, respectively. These data were combined with soil type, irrigation scheduling, drainage, and maintenance to model suitability for mosquito breeding across the landscape under three scenarios: rainy season, dry season with limited water resources, and a dry season with abundant water resources. Results: Results demonstrate seasonal, asymmetrical breeding potential and areas of maximum breeding potential as a function of environmental characteristics and anthropogenic influence in each scenario. The highest percentage of suitable area for breeding occurs during the rainy season; however, findings show that it is not merely the amount of water in an irrigated landscape, but the management of water resources that determines the aggregation of water bodies. In each scenario, timing and direction of irrigation along with inefficient drainage render the westernmost portion of BVIS the area of highest breeding opportunity, which expands and contracts seasonally in response to water resource availability and management decisions. Conclusions: Changes in the geography of breeding potential across irrigated spaces can have profound effects on the distribution of malaria risk for those living in close proximity to irrigated agricultural schemes. The methods presented are generalizable across geographies for estimating spatio-temporal distributions of breeding risk for mosquitoes in irrigated schemes, presenting an opportunity for greater geographically targeted strategies for management.

Sustainable water management is a core sustainable development goal (SDG) that also contributes to other SDGs, including food and water security, ecosystem health, and climate adaptation. To achieve these synergies, policies must target efforts to regions that best correspond with development objectives. This study designs a targeting strategy for irrigation expansion in southern Mexico—a region long considered to have strong potential for sustainable irrigation development. We use an integrated farm typology and decision tree approach to identify priority municipalities for irrigation expansion. We use multivariate statistics to examine the relationships among farm characteristics in 933 municipalities, classifying each according to four farm types: lowland, midland, midland-irrigated, and highland. We then partition municipalities into 11 farm-type subgroups, each ranked by priority level for receiving irrigation interventions following Mexico’s National Water Program guidelines. Results identify a ‘highest-priority’ subgroup of 73 municipalities comprised mostly of midland and highland farm types. These types are characterized by low irrigation use, small farmland areas, high vulnerability to climate, high marginalization (poverty), strong representation from indigenous communities, low maize yield, and high rates of subsistence production. Findings provide a crucial first approximation of where irrigation expansion would best address water policy priorities and sustainable development objectives in southern Mexico. This study also provides a useful framework for scaling organizations tasked with targeting development efforts across large spatial scales.