Has decentralisation affected child immunisation status in Indonesia?

Background: The past two decades have seen many countries, including a number in Southeast Asia, decentralising their health system with the expectation that this reform will improve their citizens’ health. However, the consequences of this reform remain largely unknown. Objective: This study analyses the effects of fiscal decentralisation on child immunisation status in Indonesia. Design: We used multilevel logistic regression analysis to estimate these effects, and multilevel multiple imputation to manage missing data. The 2011 publication of Indonesia's national socio-economic survey (Susenas) is the source of household data, while the Podes village census survey from the same year provides village-level data. We supplement these with local government fiscal data from the Ministry of Finance. Results: The findings show that decentralising the fiscal allocation of responsibilities to local governments has a lack of association with child immunisation status and the results are robust. The results also suggest that increasing the number of village health centres (posyandu) per 1,000 population improves probability of children to receive full immunisation significantly, while increasing that of hospitals and health centres (puskesmas) has no significant effect. Conclusion: These findings suggest that merely decentralising the health system does not guarantee improvement in a country's immunisation coverage. Any successful decentralisation demands good capacity and capability of local governments

ExtremeEarth meets satellite data from space

Bringing together a number of cutting-edge technologies that range from storing extremely large volumesof data all the way to developing scalable machine learning and deep learning algorithms in a distributed manner, and having them operate over the same infrastructure poses unprecedentedchallenges. One of these challenges is the integration of European Space Agency (ESA)s Thematic Exploitation Platforms (TEPs) and data information access service platforms with a data platform, namely Hopsworks, that enables scalable data processing, machine learning, and deep learning on Copernicus data, and development of very large training datasets for deep learning architectures targeting the classification of Sentinel images. In this paper, we present the software architecture of ExtremeEarth that aims at the development of scalable deep learning and geospatial analytics techniques for processing and analyzing petabytes of Copernicus data. The ExtremeEarth software infrastructure seamlessly integrates existing and novel software platforms and tools for storing, accessing, processing, analyzing, and visualizing large amounts of Copernicus data. New techniques in the areas of remote sensing and artificial intelligence with an emphasis on deep learning are developed. These techniques and corresponding software presented in thispaper are to be integrated with and used in two ESA TEPs, namely Polar and Food Security TEPs. Furthermore, we presentthe integration of Hopsworks with the Polar and Food Securityuse cases and the flow of events for the products offered through the TEPs