Study population Our basic population includes all births with a gestational age ≥37 weeks of the calendar neverless years 2002–2010 (n=1 469 955). Excluded from the final study population are all cases of antepartum mortality (n=2008). Also excluded are all elective caesarean sections, multiple pregnancies and/or inductions of labour (n=347 808). The elective caesarean sections and the inductions of labour are excluded because these obstetric interventions are not
equally distributed over the 24 h day. We have called the resulting study population a Spontaneous onset of labour, after reaching the Term period, Alive at the onset of labour, Single child (STAS) population: patients who came into the STAS (n=1 120 508). This STAS population corresponds with about 70% of the complete PRN data file. Testing the basic assumption Within the global model outlined above (figure 1) we distinguish a merged, context related patient group that is ideally suited to test the basic assumption: ‘Onset supervision of labour by midwife (1st line)’. Characteristic of this group is that it includes all patients who were assessed as low risk during pregnancy and who came
into a spontaneous onset of labour. Assuming an equal distribution of patients (records) over the 24 h day, the expected distribution over the distinct parts of the day
is as follows: daytime 29.2% (7/24), evening/night 45.8% (11/24) and duty handovers 25% (6/24). Outcomes of births The two outcome variables used in this study both have the character of adverse outcomes: the perinatal mortality rate and the incidence of the Apgar score <7 after 5 min. Partly due to the exclusion of antepartum mortality cases in the STAS population, the average incidence of perinatal mortality is especially very low—a reason for us to focus on the absolute numbers as well as the proportions (with a 95% CI) of these variables. To determine the difference in incidence of adverse outcomes between two (merged) context related patient groups, we used the risk ratio (RR) with Cilengitide a 95% CI. There are different reasons to question case-mix adjustment in a non-randomised observational evaluation study such as this.17 For example, the PRN registration does not provide for a clear and complete data set with respect to the actual risks during labour. The main reason why we have desisted from case-mix adjustment, however, is that it is not compatible with the descriptive deterministic nature of our study design. Results For the presentation of its concrete applications we use tables that are directly derived from the described model (tables 11–3).