Summary. The case notes relating to 75 of the 91 perinatal deaths of nonmalformed babies of birthweight ≥2.5 kg born in the Northern Region in 1983 were examined. The major groups involved antepartum deaths of unknown cause (40%), and deaths due to intrapartum anoxia or trauma (35%). A case‐control study compared each of the 75 cases with two controls matched for place of birth, obtained by taking the next two babies born in the same maternity unit (excluding perinatal deaths, birthweight <2.5 kg, and malformations). Four factors were found to be significantly associated with risk of perinatal death in this group: primigravidity, parity ≥3, not booked for antenatal care by 20 weeks, and corrected birthweight <3.2 kg (adjusted for gestation). Two further factors were related only to the risk of perinatal death consequent upon intrapartum events: labour post‐term and malpresentation in labour. All four factors relevant to the whole group remained independently associated with risk of perinatal death after multivariate analysis by two techniques. Adjusted odds ratios (95% CI) were estimated as: primigravidity 2.1 (1.1 to 4.1); parity three or more 5.7 (1.9 to 17); not booked for antenatal care by 20 weeks 15.7 (3.0 to 81); and corrected birthweight <3.25 kg 2.5 (1.3 to 4.6). An avoidable factor, as defined, was detected in 50% of deaths. In 30% of deaths there was an avoidable factor (grade 2) such that absence may have been expected to lead to a different outcome had all other factors remained equal. Of the avoidable factors detected, 61% related to intrapartum management, as did 76% of the grade 2 factors. Most of these involved failure to respond to evidence of fetal distress in labour. The defined group constituted 21% of all perinatal deaths, suggesting that this is an important category, particularly as their potential for normal survival should otherwise have been high.
Under the proposals in Working for Patients, Health Authorities will need to assess health needs in the populations for which they will become responsible, in order to ensure that optimal contracts are placed for hospital care. It seems inevitable that this process will have to be based at least in part on previous utilization data. Utilization data are known to be strongly influenced by the supply of facilities, particularly beds; unless this can be taken into account there is a likelihood that historical patterns will simply be perpetuated whether justified or not. We have used multiple regression analysis with nationally available data to investigate the effects of supply as well as of need and demand indicators on variations in hospital utilization rates. We describe how the approach may be taken by Health Authorities as the basis for a staged assessment of local levels of utilization, so that they may target further and more detailed investigation more efficiently.
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