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Objectives To ascertain hospital inpatient mortality in England and to determine which factors best explain variation in standardised hospital death ratios. Design Weighted linear regression analysis of routinely collected data over four years, with hospital standardised mortality ratios as the dependent variable. Setting England. Subjects Eight million discharges from NHS hospitals when the primary diagnosis was one of the diagnoses accounting for 80% of inpatient deaths. Main outcome measures Hospital standardised mortality ratios and predictors of variations in these ratios.Results The four year crude death rates varied across hospitals from 3.4% to 13.6% (average for England 8.5%), and standardised hospital mortality ratios ranged from 53 to 137 (average for England 100). The percentage of cases that were emergency admissions (60% of total hospital admissions) was the best predictor of this variation in mortality, with the ratio of hospital doctors to beds and general practitioners to head of population the next best predictors. When analyses were restricted to emergency admissions (which covered 93% of all patient deaths analysed) number of doctors per bed was the best predictor. Conclusion Analysis of hospital episode statistics reveals wide variation in standardised hospital mortality ratios in England. The percentage of total admissions classified as emergencies is the most powerful predictor of variation in mortality. The ratios of doctors to head of population served, both in hospital and in general practice, seem to be critical determinants of standardised hospital death rates; the higher these ratios, the lower the death rates in both cases.

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Underprivileged areas were identified by weighting several census variables that relate to social conditions, by using weights determined by means of a questionnaire sent to one in 10 of the general practitioners in the United Kingdom. The weighted variables were added (after statistical manipulation) to give a score for each of the 9265 electoral wards in England and Wales. Blank ward maps were sent to general practitioners in five family practitioner committee areas and they were asked to shade the wards according to the degree to which the population increased their workload or the pressure on their services. Maps of these same areas were then prepared by using the calculated scores with the cut off points between the worst, the intermediate, and the best areas as on those used by the general practitioners. The two sets of maps were then compared to determine how well the maps that were based on scores agreed with the general practitioners' maps showing their assessment of the variation of workload in their areas. Overall, 6.3% of the wards differed in shading in any way between the two sets of maps. In the three areas where the general practitioners shaded complete wards and did not report having difficulties with shading only 1.2% of the wards differed. It may be possible to use these "underprivileged area" scores to indicate where problems occur for general practitioners and to extend this work to other primary health care workers.

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