Hospital Episode Statistics (HES)
This study used a retrospective design to explore the feasibility of monitoring inequalities within an acute trust. We obtained routinely collected administrative data, HES, from April 2007- March 2010, from the NHS Connecting for Health secondary uses service (SUS). We hold Section 251 National Information Governance Board for Health and Social Care permission to hold these data for research purposes. We also hold South East Local Research Ethics Committee approval to analyse the data. HES have been collected on all patients admitted to NHS hospitals since 1989 and include demographic, diagnostic and procedural data . Three years of data were used to gain the largest numbers of patients whilst minimising additional confounding because of changes over time in coding, medical practice or policy, although coding may have improved over the course of these three years which we cannot control for .
HES records represent the finished consultant episode – “a period of admitted patient care under a consultant or allied healthcare professional within an NHS trust” . A stay in a hospital can be made up of one or multiple finished consultant episodes. These were linked together into admissions which is the unit of analysis used here. Transfers in from other hospitals were not included. A proportion of the admissions considered in this study would have ended as transfers, however post-transfer length of stay and readmissions were not considered in this study.
Procedures and database inclusions
Inequalities were examined within procedure groups rather than specialities so that patients undergoing similar procedures were compared to each other. Although patients can have multiple procedures within an admission, patients were grouped by their main operative procedure, the most resource intensive procedure of the admission . This study looked at elective admissions for seven procedures - bariatric surgery, cholecystectomy, coronary angioplasty, primary hip replacement, inguinal hernia repairs, primary knee replacement and mastectomy (for breast cancer in women). These elective surgical procedures were chosen in discussion with service leads to include the most common, and those that were rapidly increasing in volume and those where inequalities have been reported from national data. By including common procedures we would increase the statistical power to identify inequalities should any exist. Non-elective patients, day admissions and patients under 18 were excluded from the analysis to try to reduce the variability in the sample and to take into account some of the case-mix within the procedure groups.
Measures of inequalities
This study explored variations in access, process and outcome measures by age, sex, ethnicity and social deprivation. Data on age, sex, ethnicity and patient postcode are routinely included within HES. Data on ethnicity, however, is often recorded as not stated or not known, though this improved nationally from 24% missing in 2004 to 9% in 2010 . Due to small numbers in some groups, age bands were combined into 3 to 5 groups, based on the distribution of age for each procedure area. Ethnicity categories within HES are based on the ethnic groups used in the 2001 census . Small numbers made it necessary to combine these into four categories – white, Asian, black and other and mixed backgrounds. The Carstairs index of deprivation was used to determine the social deprivation of the postcode of the patient’s home address as a proxy for the patient’s socioeconomic status . The Carstairs index was used in the dataset as it is available at a smaller area level, the lower super output area. The scores in the original dataset were split into five population-weighted quintiles based on the national distribution. These were combined into 2 groups, quintiles 1 to 3 and quintiles 4 to 5, to provide sufficient numbers and enable comparison between the more and less deprived.
In addition to the four socio-demographic variables which were used to explore inequalities, data on comorbidities were used to take case-mix into account. The dataset included a measure of comorbidity. Each patient had a primary diagnosis, any other secondary diagnoses or comorbidities were used to derive a comorbidity score using the Charlson comorbidity index, taking into account both the number and severity of the comorbidities that a patient might have . The weights used were derived from English administrative hospital data . The comorbidity score was dichotomized into a binary variable ‘no comorbidities’ or ‘one or more comorbidities’.
Dependent variables: process and outcome measures
The process and outcome measures of hospital care used were waiting times, length of stay and readmissions. Waiting time is the time between the date on which the patient was put on the waiting list and the date on which they were admitted, and therefore includes any time when an individual might be suspended from the waiting list, a patient does not attend or if a patient is unable to have surgery because of ill health . It is only valid for elective patients with planned admissions; non-elective patients were therefore excluded from all analyses conducted for this study. Length of stay represents the number of days the patient spends in the hospital during their admission. The continuous variables length of stay and waiting times were tested for normality. Common normalising transformations of the data, such as the reciprocal, square root and natural log were unsuccessful and binary variables were therefore created from these continuous variables. Under the NHS constitution patients should not wait more than 18 weeks from referral for treatment, however this could not be used to define a prolonged waiting time as too few people waited longer than this time . 75th percentiles were therefore used for each procedure group. The 75th percentile was also used to define a prolonged length of stay. A similar study looking at waiting times used the median as a cut-off point but we used the 75th percentile as the tail of the distribution was of more interest . 75th percentiles have been used in other studies to define a prolonged length of stay . Readmissions were measured using the derived field of unplanned readmissions within 28 days of discharge. Those patients who died were excluded from the analysis when readmissions were analysed. Readmissions were not explored for inguinal hernia repairs as there were too few readmissions.
The data analysis was conducted using SPSS v.18 (SPSS Inc, Chicago, Illinois, USA). Statistical significance was set at p ≤ 0.01, to take the multiple analyses into account. Descriptive statistics were used to examine the distribution of the variables for the whole population and for each procedure group.
Logistic regression was used for each procedure group to explore the relationship between each of the explanatory variables (ethnicity, social deprivation, age, sex) and the process or outcome measure. Multiple logistic regression was then used to explore the independent effects of the explanatory variables and to adjust for comorbidity.
Power and sample size calculations
Power is the probability of rejecting a false null hypothesis i.e. it is the ability of the test to find an effect that is there. Power calculations were conducted retrospectively for one of the analyses - the relationship between social deprivation and a prolonged wait for coronary angioplasty, as Pell and Pell et al.’s 2000 study found a similar relationship . Sample size calculations were also made, using this study as a pilot, to determine how many years of data would be needed to detect an effect.
Power and sample size calculations were calculated retrospectively using G*Power 3.1.2. For logistic regression this program uses the methodology described in Hsieh, Block & Larsen’s 1997 paper . Rather than using complex calculations for logistic regression, this method is based on comparing proportions and then adjusting for a multifactorial model by a variance inflation factor .