Primary research
Hospital characteristics affecting potentially avoidable emergency admissions: National ecological study
Health Services Management Research 2013, Vol. 26(4) 110–118 ! The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0951484814525357 hsm.sagepub.com
A O’Cathain1, E Knowles1, R Maheswaran1, J Turner1, E Hirst2, S Goodacre1, T Pearson1 and J Nicholl1
Abstract Some emergency admissions can be avoided if acute exacerbations of health problems are managed by emergency and urgent care services without resorting to admission to a hospital bed. In England, these services include hospitals, emergency ambulance, and a range of primary and community services. The aim was to identify whether characteristics of hospitals affect potentially avoidable emergency admission rates. An age-sex adjusted rate of admission for 14 conditions rich in avoidable emergency admissions was calculated for 129 hospitals in England for 2008–2011. Twenty-two per cent (3,273,395/14,998,773) of emergency admissions were classed as potentially avoidable, with threefold variation between hospitals. Explanatory factors of this variation included those which hospital managers could not control (demand for hospital emergency departments) and those which they could control (supply in terms of numbers of acute beds in the hospital, and management of non-emergency and emergency patients within the hospital). Avoidable admission rates were higher for hospitals with higher emergency department attendance rates, higher numbers of acute beds per 1000 catchment population and higher conversion rates from emergency department attendance to admission. Hospital managers may be able to reduce avoidable emergency admissions by reducing supply of acute beds and conversion rates from emergency department attendance.
Keywords access and evaluation, emergency treatment, health care quality, variation analysis
Introduction The recent increase in the numbers of emergency admissions1 raises concern about whether all such admissions are necessary. Unnecessary admissions place pressures on hospitals and inconvenience patients and their families. Identifying unnecessary admissions is challenging and different definitions exist. One definition is based on preventability, identifying ambulatory and primary care-sensitive conditions where emergency admissions can be prevented through intervention in primary care.2,3 For example, primary care specialist diabetes nurses monitor and educate diabetes patients to prevent exacerbations which might lead to emergency admissions. An alternative definition is based on avoidability. This is where a person has an acute health problem, or an exacerbation of an existing health problem, which can be dealt with by services in the emergency and urgent care system without resort to emergency admission. For example, a hypoglycaemic
episode is dealt with at a patient’s home by the ambulance service so that an emergency admission is not required. The responsibility for avoiding emergency admissions can lie beyond primary care, with the range of services in the wider system of emergency and urgent care that respond to patients suffering an acute health problem.4,5 In England currently, these services include emergency ambulances, emergency departments, general practice out of hours services, same day appointments in general practice, walk-in centres and community services such as district nursing. Hospitals are part of this emergency and urgent care 1 School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK 2 Sheffield Emergency Care Forum, Sheffield, UK
Corresponding author: A O’Cathain, School of Health and Related Research (ScHARR), University of Sheffield, 30 Regent Street, Sheffield S1 4DA, UK. Email: [email protected]
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system and how they are managed may affect rates of avoidable emergency admissions. Only some admissions can potentially be avoided through intervention from services in an emergency and urgent care system. Fourteen clinical conditions have been identified as rich in avoidable emergency admissions through consensus methods with 48 clinicians and researchers with a special interest in emergency and urgent care.6 These conditions include nonspecific chest pain, non-specific abdominal pain, chronic obstructive pulmonary disease, minor head injuries, falls and urinary tract infections. There is considerable overlap between our 14 conditions and ambulatory and primary care-sensitive conditions – diabetes complications, chronic obstructive pulmonary disease, angina, cellulitis and epileptic fits3 – while our 14 conditions also include injuries such as minor head injuries and falls as well as illnesses. In a related study, we used admissions from these 14 conditions to calculate an age-sex adjusted avoidable admission rate for 150 emergency and urgent care systems in England defined by geographically based populations served by health care commissioning bodies called ‘primary care trusts’.7 We found more than 3-fold variation in age-sex standardised avoidable admission rates for these 150 emergency and urgent care systems. We used routinely available data on population, geography and quality of care of different services to test which characteristics of emergency and urgent care systems affected age-sex adjusted avoidable admission rates. We found that population and geographical factors explained 75% of variation in age-sex adjusted avoidable admission rates between systems, with areas of high unemployment and major urban status having higher avoidable admission rates. High unemployment was correlated with prevalence of some of the 14 conditions. A study of preventable admissions in the United States found that a similarly large amount of variation was explained by the prevalence of disease and low income.3 We also found that characteristics of hospitals (including their emergency departments), emergency ambulance services and general practice explained further variation. Areas with high attendance rates at emergency departments, high conversion rates of attendances to admissions, high percentages of emergency admissions staying less than a day, low percentages of emergency ambulance calls not transported to hospital and high percentages of perceived access to general practice within 48 h had high avoidable admissions rates.7 Hospitals are often the focus for efforts to address avoidable admissions. However, in our previous study, we found routine data on a limited number of hospital characteristics for the ‘primary care trust’ populations we were studying. We found that most of the hospital characteristics we tested were important for explaining
variation in potentially avoidable admission rates: the conversion rate of emergency department attendances to admission, and the proportion of all emergency admissions with length of stay of less than one day but not the waiting time for planned admissions.7 These characteristics related to how attendances were managed within the emergency department and other referral routes to a hospital bed. We were aware of data available for hospitals which were not routinely available for ‘primary care trusts’, for example, data on the number of acute beds. We were also aware that researchers have tended to explore the factors affecting emergency admission rates overall, or for specific conditions, through studying variation between general practices or health care commissioning organisations8–13 rather than between hospitals. Researchers exploring hospital characteristics have found some of them to be important. The amount of floor space in hospitals but not the numbers of acute beds explained variation in ambulatory care-sensitive conditions in the United States,3 while bed availability explained variation in overall admission rates after adjusting for socio-economic status and disease burden.14 Our aim was to calculate age-sex adjusted avoidable admission rates for hospitals in England and to test the effect of a wider range of hospital characteristics on variation in these rates than tested in previous studies. We aimed to distinguish between factors which are and are not in the control of hospital managers to allow hospital managers to consider any actions they can take to reduce avoidable admissions.
Methods Calculation of the age-sex adjusted avoidable admission rate The age-sex adjusted avoidable admission rate is based on 14 health conditions which are rich in avoidable emergency admissions.6 Twenty-nine per cent of these admissions occurred in over 75 year olds.7 Numbers of admissions to each hospital for the set of 14 conditions were calculated using Hospital Episode Statistics for the three financial years April 2008 to March 2011. Catchment populations for hospitals were needed to calculate admission rates for these 14 conditions. There are different ways of calculating catchment populations for hospitals, with debates about which approach is best.15,16 We used estimates of hospital catchment populations for emergency admissions in 2009 calculated by Public Health Observatories in England.17 These catchment areas were defined as the number of people in each sex and age group who live in the catchment of the hospital. They were calculated using Hospital Episode Statistics data between April 2006
Downloaded from hsm.sagepub.com by guest on November 14, 2015
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and March 2009 to count the number of emergency admissions in each age and sex group from small areas called Middle Super Output Areas. These areas have a minimum population of 5000, with an overall mean of 7200. The Office for National Statistics supplied the 2009 mid-year population estimates. Within each five-year age and sex group, the proportion of emergency admissions that went to each hospital as a proportion of those who used any hospital was calculated. For each small area, this proportion was multiplied by the resident population in that age-sex group to give the small area catchment population for each hospital. Then, the small area catchment populations for each hospital were summed to give the total catchment population for each hospital. We calculated the directly age-sex standardised admission rates per 100,000 per year for each hospital for the three-year period April 2008 to March 2011 using seven age groups (0–4, 5–14, 15–44, 45–64, 65–74, 75–84, 85þ) standardised to the whole population for England in 2009. A three-year period was selected to ensure that the effect of annual variability in emergency admission rates was minimised. We call this the ‘potentially avoidable admission rate’ in recognition that some of these admissions would need a hospital bed even with the best of management by all services in the emergency and urgent care system.
Inclusion criteria for hospitals In England, hospitals are known as Trusts and can include a number of buildings based at separate sites, with some of these buildings known as hospitals by their local communities. The Trust manages all the hospitals within it, operating as a single organisation, and this is the unit of analysis in this paper. We use the term ‘hospital’ rather than ‘Trust’ throughout the paper to facilitate international relevance. Some specialist hospitals offer care to specific age, sex or condition groups only. We wanted to compare similar types of hospitals and focused on general hospitals which admit all types of patients. Therefore, we included any hospitals where the estimated population in each age-sex group was >1000. This was an arbitrary cut-off point which successfully excluded children’s hospitals, women’s hospitals and condition-specific hospitals. It also excluded some general hospitals located near children’s hospitals because they did not admit children.
Characteristics of hospitals We searched databases for routinely available data on the characteristics of hospitals in England. Ideally, we wanted to include characteristics of the hospital catchment populations because deprivation has been shown
to explain large amounts of variation in emergency and avoidable emergency admissions.7,18–21 A measure of deprivation for the catchment population was not available routinely but a proxy was: the deprivation level of the population in the geographical area in which the hospital was located. We used the postcode of the emergency department of each hospital and identified the percentage of households in poverty in the area around the postcode using the Office of National Statistics data on Middle Super Output Areas. Where there were two or more emergency departments based at different geographical sites, we took the mean of these percentages. We found routine data on demand, supply, accessibility and management variables for hospitals in England (see Table 1). We wanted to include some factors but could find no routinely available data on them for all hospitals, for example, financial stability.
Analysis We undertook linear regression in IBM SPSS Statistics version 21 weighted for hospital catchment population to account for larger uncertainty of estimates for smaller populations. The dependent variable was the age-sex adjusted emergency admission rate for conditions rich in avoidable admissions per 100,000 population per year between 2008 and 2011. The independent variables were tested in a hierarchical multiple regression in two blocks, using forward stepwise regression within each block. Variables were included if the p-value for the ttest was
Hospital characteristics affecting potentially avoidable emergency admissions: National ecological study
Health Services Management Research 2013, Vol. 26(4) 110–118 ! The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0951484814525357 hsm.sagepub.com
A O’Cathain1, E Knowles1, R Maheswaran1, J Turner1, E Hirst2, S Goodacre1, T Pearson1 and J Nicholl1
Abstract Some emergency admissions can be avoided if acute exacerbations of health problems are managed by emergency and urgent care services without resorting to admission to a hospital bed. In England, these services include hospitals, emergency ambulance, and a range of primary and community services. The aim was to identify whether characteristics of hospitals affect potentially avoidable emergency admission rates. An age-sex adjusted rate of admission for 14 conditions rich in avoidable emergency admissions was calculated for 129 hospitals in England for 2008–2011. Twenty-two per cent (3,273,395/14,998,773) of emergency admissions were classed as potentially avoidable, with threefold variation between hospitals. Explanatory factors of this variation included those which hospital managers could not control (demand for hospital emergency departments) and those which they could control (supply in terms of numbers of acute beds in the hospital, and management of non-emergency and emergency patients within the hospital). Avoidable admission rates were higher for hospitals with higher emergency department attendance rates, higher numbers of acute beds per 1000 catchment population and higher conversion rates from emergency department attendance to admission. Hospital managers may be able to reduce avoidable emergency admissions by reducing supply of acute beds and conversion rates from emergency department attendance.
Keywords access and evaluation, emergency treatment, health care quality, variation analysis
Introduction The recent increase in the numbers of emergency admissions1 raises concern about whether all such admissions are necessary. Unnecessary admissions place pressures on hospitals and inconvenience patients and their families. Identifying unnecessary admissions is challenging and different definitions exist. One definition is based on preventability, identifying ambulatory and primary care-sensitive conditions where emergency admissions can be prevented through intervention in primary care.2,3 For example, primary care specialist diabetes nurses monitor and educate diabetes patients to prevent exacerbations which might lead to emergency admissions. An alternative definition is based on avoidability. This is where a person has an acute health problem, or an exacerbation of an existing health problem, which can be dealt with by services in the emergency and urgent care system without resort to emergency admission. For example, a hypoglycaemic
episode is dealt with at a patient’s home by the ambulance service so that an emergency admission is not required. The responsibility for avoiding emergency admissions can lie beyond primary care, with the range of services in the wider system of emergency and urgent care that respond to patients suffering an acute health problem.4,5 In England currently, these services include emergency ambulances, emergency departments, general practice out of hours services, same day appointments in general practice, walk-in centres and community services such as district nursing. Hospitals are part of this emergency and urgent care 1 School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK 2 Sheffield Emergency Care Forum, Sheffield, UK
Corresponding author: A O’Cathain, School of Health and Related Research (ScHARR), University of Sheffield, 30 Regent Street, Sheffield S1 4DA, UK. Email: [email protected]
Downloaded from hsm.sagepub.com by guest on November 14, 2015
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system and how they are managed may affect rates of avoidable emergency admissions. Only some admissions can potentially be avoided through intervention from services in an emergency and urgent care system. Fourteen clinical conditions have been identified as rich in avoidable emergency admissions through consensus methods with 48 clinicians and researchers with a special interest in emergency and urgent care.6 These conditions include nonspecific chest pain, non-specific abdominal pain, chronic obstructive pulmonary disease, minor head injuries, falls and urinary tract infections. There is considerable overlap between our 14 conditions and ambulatory and primary care-sensitive conditions – diabetes complications, chronic obstructive pulmonary disease, angina, cellulitis and epileptic fits3 – while our 14 conditions also include injuries such as minor head injuries and falls as well as illnesses. In a related study, we used admissions from these 14 conditions to calculate an age-sex adjusted avoidable admission rate for 150 emergency and urgent care systems in England defined by geographically based populations served by health care commissioning bodies called ‘primary care trusts’.7 We found more than 3-fold variation in age-sex standardised avoidable admission rates for these 150 emergency and urgent care systems. We used routinely available data on population, geography and quality of care of different services to test which characteristics of emergency and urgent care systems affected age-sex adjusted avoidable admission rates. We found that population and geographical factors explained 75% of variation in age-sex adjusted avoidable admission rates between systems, with areas of high unemployment and major urban status having higher avoidable admission rates. High unemployment was correlated with prevalence of some of the 14 conditions. A study of preventable admissions in the United States found that a similarly large amount of variation was explained by the prevalence of disease and low income.3 We also found that characteristics of hospitals (including their emergency departments), emergency ambulance services and general practice explained further variation. Areas with high attendance rates at emergency departments, high conversion rates of attendances to admissions, high percentages of emergency admissions staying less than a day, low percentages of emergency ambulance calls not transported to hospital and high percentages of perceived access to general practice within 48 h had high avoidable admissions rates.7 Hospitals are often the focus for efforts to address avoidable admissions. However, in our previous study, we found routine data on a limited number of hospital characteristics for the ‘primary care trust’ populations we were studying. We found that most of the hospital characteristics we tested were important for explaining
variation in potentially avoidable admission rates: the conversion rate of emergency department attendances to admission, and the proportion of all emergency admissions with length of stay of less than one day but not the waiting time for planned admissions.7 These characteristics related to how attendances were managed within the emergency department and other referral routes to a hospital bed. We were aware of data available for hospitals which were not routinely available for ‘primary care trusts’, for example, data on the number of acute beds. We were also aware that researchers have tended to explore the factors affecting emergency admission rates overall, or for specific conditions, through studying variation between general practices or health care commissioning organisations8–13 rather than between hospitals. Researchers exploring hospital characteristics have found some of them to be important. The amount of floor space in hospitals but not the numbers of acute beds explained variation in ambulatory care-sensitive conditions in the United States,3 while bed availability explained variation in overall admission rates after adjusting for socio-economic status and disease burden.14 Our aim was to calculate age-sex adjusted avoidable admission rates for hospitals in England and to test the effect of a wider range of hospital characteristics on variation in these rates than tested in previous studies. We aimed to distinguish between factors which are and are not in the control of hospital managers to allow hospital managers to consider any actions they can take to reduce avoidable admissions.
Methods Calculation of the age-sex adjusted avoidable admission rate The age-sex adjusted avoidable admission rate is based on 14 health conditions which are rich in avoidable emergency admissions.6 Twenty-nine per cent of these admissions occurred in over 75 year olds.7 Numbers of admissions to each hospital for the set of 14 conditions were calculated using Hospital Episode Statistics for the three financial years April 2008 to March 2011. Catchment populations for hospitals were needed to calculate admission rates for these 14 conditions. There are different ways of calculating catchment populations for hospitals, with debates about which approach is best.15,16 We used estimates of hospital catchment populations for emergency admissions in 2009 calculated by Public Health Observatories in England.17 These catchment areas were defined as the number of people in each sex and age group who live in the catchment of the hospital. They were calculated using Hospital Episode Statistics data between April 2006
Downloaded from hsm.sagepub.com by guest on November 14, 2015
112
Health Services Management Research 26(4)
and March 2009 to count the number of emergency admissions in each age and sex group from small areas called Middle Super Output Areas. These areas have a minimum population of 5000, with an overall mean of 7200. The Office for National Statistics supplied the 2009 mid-year population estimates. Within each five-year age and sex group, the proportion of emergency admissions that went to each hospital as a proportion of those who used any hospital was calculated. For each small area, this proportion was multiplied by the resident population in that age-sex group to give the small area catchment population for each hospital. Then, the small area catchment populations for each hospital were summed to give the total catchment population for each hospital. We calculated the directly age-sex standardised admission rates per 100,000 per year for each hospital for the three-year period April 2008 to March 2011 using seven age groups (0–4, 5–14, 15–44, 45–64, 65–74, 75–84, 85þ) standardised to the whole population for England in 2009. A three-year period was selected to ensure that the effect of annual variability in emergency admission rates was minimised. We call this the ‘potentially avoidable admission rate’ in recognition that some of these admissions would need a hospital bed even with the best of management by all services in the emergency and urgent care system.
Inclusion criteria for hospitals In England, hospitals are known as Trusts and can include a number of buildings based at separate sites, with some of these buildings known as hospitals by their local communities. The Trust manages all the hospitals within it, operating as a single organisation, and this is the unit of analysis in this paper. We use the term ‘hospital’ rather than ‘Trust’ throughout the paper to facilitate international relevance. Some specialist hospitals offer care to specific age, sex or condition groups only. We wanted to compare similar types of hospitals and focused on general hospitals which admit all types of patients. Therefore, we included any hospitals where the estimated population in each age-sex group was >1000. This was an arbitrary cut-off point which successfully excluded children’s hospitals, women’s hospitals and condition-specific hospitals. It also excluded some general hospitals located near children’s hospitals because they did not admit children.
Characteristics of hospitals We searched databases for routinely available data on the characteristics of hospitals in England. Ideally, we wanted to include characteristics of the hospital catchment populations because deprivation has been shown
to explain large amounts of variation in emergency and avoidable emergency admissions.7,18–21 A measure of deprivation for the catchment population was not available routinely but a proxy was: the deprivation level of the population in the geographical area in which the hospital was located. We used the postcode of the emergency department of each hospital and identified the percentage of households in poverty in the area around the postcode using the Office of National Statistics data on Middle Super Output Areas. Where there were two or more emergency departments based at different geographical sites, we took the mean of these percentages. We found routine data on demand, supply, accessibility and management variables for hospitals in England (see Table 1). We wanted to include some factors but could find no routinely available data on them for all hospitals, for example, financial stability.
Analysis We undertook linear regression in IBM SPSS Statistics version 21 weighted for hospital catchment population to account for larger uncertainty of estimates for smaller populations. The dependent variable was the age-sex adjusted emergency admission rate for conditions rich in avoidable admissions per 100,000 population per year between 2008 and 2011. The independent variables were tested in a hierarchical multiple regression in two blocks, using forward stepwise regression within each block. Variables were included if the p-value for the ttest was
