M. M a˚lqvist increased spread of ultrasound has in recent years led to a sex ratio at birth well above normal variations (30). The proximate determinants in the model are divided into five categories: maternal factors, health system factors, neonatal factors, delivery factors, and postnatal care. In most models, there is no hierarchy among the proximate determinants and it is not strictly in this model either. However, by putting maternal and health system factors above the other groups acknowledges the circum- stance that these two have a significant impact not only on the outcome but also on the three other groups. Characteristics of the mother are of great importance when it comes to birth weight and even the sex of the newborn. The level of female emancipation may decide where the delivery takes place and who is assisting (31, 32). Further, characteristics of the health system, for example attitudes and availability, greatly influence delivery place (33) and the postnatal care (34, 35). Notable is that not all arrows between proximate determinants are two-way. The characteristics of post- natal care do not, for example, influence the neonatal factors, whereas the sex of the baby may have a considerable impact on the postnatal care. Maternal factors are not easily affected by the other groups but, of course, have a major influence on the choice of delivery place and the quality of postnatal care. In the end, the difference between death and survival in the neonatal period is dependent upon what responses are given to the newborn’s needs and to the threats to the neonates’ health. Most of the interventions needed to save newborn lives are not complicated, costly, or high- tech but, instead, simple and straightforward with a potential of reducing mortality with up to two-thirds (8). With the addition of resources and the latest medical technology, it is possible to save almost all newborns, as statistics from Singapore exhibit, further emphasising the importance of actions taken or not taken (36). The interventions in the model are examples based on priorities made by WHO. Methods NeoKIP The studies here presented are parts of a population- based cluster randomised study called NeoKIP (Neonatal Health Knowledge Into Practice, ISRCTN44599712), initiated by Uppsala University in Sweden, the Ministry of Health in Vietnam and the Vietnam Sweden hospital in Uong Bi, Vietnam. A baseline survey in the whole province was performed in 2006, registering all births and neonatal deaths occurring in 2005. The intervention of the NeoKIP study commenced in July 2008. Study setting Quang Ninh province (QN) is situated in the north-east corner of Vietnam, sharing a border with China in the north. The province inhabits a little more than a million people and is currently undergoing rapid economic development. In Quang Ninh province, there are 10 different ethnic groups (Kinh, Dao, San Chi, San Diu, Tay, Hoa, Cao Lang, Thanh Y, Thanh Phan, and Chinee) with Kinh being the majority group constituting about 80% of the population (37). There are 14 districts in Quang Ninh province, of these, 8 districts had a neonatal mortality rate (NMR) higher than 15/1,000 2005 accord- ing to NeoKIP baseline results and were identified and chosen to constitute the study area (Fig. 2). In 2005 there Fig. 2. Quang Ninh province in northern Vietnam. 4 (page number not for citation purpose) Citation: Global Health Action 2011, 4: 5724 - DOI: 10.3402/gha.v4i0.5724