NHS Digital Data Release Register - reformatted

The Institute for Fiscal Studies (IFS)

Opt outs honoured: N

Basis: Health and Social Care Act 2012

Format: Anonymised - ICO code compliant Non Sensitive

How often: One-Off

When: unknown — 11/2016

HSCIC Id: DARS-NIC-17824-V9F2B-v0.5

Data: Hospital Episode Statistics Admitted Patient Care

Project info

Output: (1) There will be three written outputs: (i) an IFS working paper produced in the next year, which will be available on the IFS website and read by all those who use the website including government departments and academics; (ii) an academic economics journal article submitted to the Economic Journal. The Economic Journal is an international peer-reviewed Economics journal with an impact factor of 2.587 and over 900,000 article downloads in 2014. The principal audience is economics academics who will read and cite the paper; (iii) a non-technical research summary which will receive a press-release and target policy makers from the Department of Health, Monitor, NHS England and the CQC in the next year. Other outputs will include presentations at academic conferences such as the European Economists Association (EEA) Conference, which will focus on receiving comments from economists on how to improve the analysis, and presentations to policy makers involved in the planning and delivery of NHS care.

(2) The outputs for this project are similar to those for project (1). The aims are to produce written outputs that are widely cited in the academic literature and encourage more academic work on the NHS, and non-technical summaries that will provide information for policy makers that would otherwise be unavailable or very costly for the Department of Health or Monitor to acquire. As this project is more complex, IFS expect outputs in the next 1-3 years. The model IFS produce will have the capacity to model potential policy scenarios. IFS will interact with the Department of Health to ascertain whether there are any policy scenarios they would like IFS to model to maximize the value to the Health and Social Care system. Other outputs will include presentations to academics at conferences such as the Royal Economic Society Conference, in order to refine this model, and presentations to representatives from the Department of Health, Monitor, NHS England and the CQC to understand how this model could be used to inform policy.

(3) The principal output of this work will be an article submitted to the Journal of the American Medical Association (JAMA) later this year (postponed from 2015, due to a delay in receiving US data). This is a highly respected international peer-reviewed medical journal and is the most widely circulated medical journal in the world, with a print and electronic annual circulation of more than 320,000 and 1.2 million subscribers respectively. IFS will also publish a policy briefing note aimed at policy makers in England, which will be freely available on the IFS website. Publication will be accompanied by a press release, directed at NICE, DH and NHS England.

(4) Some of these outputs from this project, including several conference presentations, a policy presentation and a non-technical policy summary, have already been produced under the previous license agreement. Previous conference presentations included a workshop attended by representatives from the Department of Health, Monitor, the Nuffield Trust, the Office for Health Economics, and the Kings Fund, and economics academic conferences including the Royal Economic Society conference. IFS will submit a peer-reviewed journal article to the Journal of Health Economics, the leading academic publication on health economics, this year.

(5) The principal outputs will be (i) a working paper, published under the IFS working paper series (see project 1), (ii) an academic conference presentation, (iii) a peer-reviewed journal article in an economics journal (specifically, as part of a special issue of Fiscal Studies on cross-country comparisons of health spending across the lifecycle) in 2016, and a non-technical, policy summary. IFS will present the results in dissemination events with policymakers, funded and hosted by the Health Foundation. Fiscal Studies is a peer-reviewed economics general with all articles explicitly aimed at bridging the gap between academic research and policy, with a reputation for publishing timely high-quality articles that are easily accessible to policymakers. A workshop to discuss preliminary findings took place in March 2015. This workshop was attended by representatives from the Department of Health, who subsequently invited IFS to present the findings at the Department. IFS have spoken to the OECD about this work, who believes it could help inform their highly influential work on cross-country comparisons of health systems. The Health Foundation event will be exclusively focused on policymakers, with invites to representatives of DH, Monitor and PCTs, along with members of influential health policy research groups such as the Kings Fund and Nuffield Trust. IFS expect outputs from this project in 2016 and 2017.

(6) The principal outputs will be (i) a working paper, published under the IFS working paper series (by Spring 2016), (ii) an academic conference presentation at the Annual European Economists Association (EEA) Conference in August 2015, and (iii) the submission of a peer-reviewed journal article to the Economic Journal. The benefits of these outputs are discussed above. The EEA Conference is attended by the leading economists in Europe and the USA. IFS will produce non-technical policy summaries for policymakers and send these reports to representatives of Department of Health, Monitor and NHS England.

(7) In the first year of the project, the main outputs will be an interim report submitted to the funder: due in July 2016 on the impact analysis of the determinants of the rollout of Sure Start. In the second year, IFS will have three main outputs: (1) the final report, to be submitted to the Nuffield Foundation in February 2017; (2) an IFS working paper (see above), and (3) a related academic paper. Both report and paper will be available on the IFS website. The academic paper will be targeted to a top economic journal, such as the Economic Journal (see above). The findings of the report will be disseminated by press release and an IFS policy observation (on the IFS website) in order to reach target audiences in the media and general public. A launch event will be organised at IFS, where the results will be presented and academics will be invited (experts in early years policy) and policy makers (MPs working on early years policy such as Frank Field and Tim Loughton of the All Party Parliamentary Group) to discuss their implications. Finally, IFS will present the findings at major economics conferences (such as the Royal Economic Society, RES), to gain comments from other academics working in the same field before submission to a top-tier peer-reviewed economic journal.

(8) IFS expect written output within the next 1-3 years. This will take the following form: an academic economics journal article submitted to the American Economic Journal: Economic Policy (AEJ-EP) or the Economic Journal (EJ). Both of these journals are leading international peer-reviewed Economics journals. The principal audience is economics academics who will read and cite the paper. Other outputs will include presentations at academic conferences and seminar presentations, which will focus on receiving comments from economists on how to improve the analysis.

(9) The project is expected to produce a range of outputs, including (i) an IFS working paper (see above), published in early 2017; (ii) multiple academic conference presentations to general economics (e.g. the Annual Royal Economics Society conference) and health economics audiences (e.g. the annual meeting of the UK Health Economists Study Group); (iii) the submission of a journal article to a leading peer-review economics journal, such as the Review of Economic Studies (Impact Factor: 4.038) or the Journal of Health Economics (IF: 2.579); (iv) a non-technical policy summary, which will be press released and sent to contacts at the Department of Health and NHS England. The Health Economists Study Group is a work-in-progress conference attended by the leading health economists in England, and representatives from NHS England, the Department of Health and leading health policy organisations such as the Health Foundation and the Kings Fund. Their comments will give the researchers the chance to improve the analysis and focus the findings in the most informative way for policy.

In the first year, the main outputs will be presentations and discussions with the Department of Health and NHS England to check the validity of the assumptions underlying the model that IFS estimate, and to identify where the model could provide information and simulations that are useful to policy-makers. As the estimation of the model is reasonably complex, written outputs are expected over the next 1-3 years. These will take three forms: (i) an IFS working paper, which will be available on the IFS website and read by all those who use the website including government departments and academics; (ii) an academic economics journal article submitted to the Economic Journal. The Economic Journal is an international peer-reviewed Economics journal with an impact factor of 2.587 and over 900,000 article downloads in 2014. The principal audience is economics academics who will read and cite the paper; (iii) a non-technical research summary which will receive a press-release and target policy makers from the Department of Health, Monitor, NHS England and the CQC in the next year. Other outputs will include presentations at academic conferences such as the European Economists Association (EEA) Conference, which will focus on receiving comments from economists on how to improve the analysis, and presentations to policy makers involved in the planning and delivery of NHS care.

Activities: (1) Part 1 will model changes in the probability that a woman gives birth in her nearest maternity hospital) over the past decade. IFS will assess whether women with certain characteristics (older, or from different types of areas) are more likely to bypass their nearest hospital, or whether patients travel further for maternity care at a teaching hospital. Part 2 will focus on patients who have at least two HES maternity records, and examine whether care offered during the birth of the first child affects the mother’s choice of hospital for subsequent births. There are a range of factors that could affect both the ease of childbirth and where a mother decides to give birth to subsequent children that are unrelated to the care she received, for example, her age. IFS will therefore isolate random variations in treatment, which could be used to identify the impact of health care provision. Possible examples include whether the mother gives birth at a weekend or public holiday, the number of other babies born at the same hospital on the same day. The data will be used to create a sample of women who gave birth in NHS hospitals. The pseudonymised ID will be used to identify whether, where and when these women give birth again. The data will then be used to estimate statistical models to assess which factors around the time of the first birth affected the subsequent patterns that are identified. All work is conducted using the statistical software package Stata.

The HES data may be linked to aggregated geographical data relating patient or hospital. Examples include the number of women of childbearing or number of hospitals within the local area. These data do not contain any additional information about the individuals themselves. Any additional data would be added to the statistical model.
An example of a publicly available geographical data to be linked would be ONS population statistics (http://www.ons.gov.uk/ons/publications/re-reference-tables.html?edition=tcm%3A77-315018). HES data may be linked to aggregated data at the level of the hospital (for example, whether the hospital has an alongside maternity unit) or the geographical area of the patient/hospital (For example the number of hospitals within the local area). Again, these data do not contain any additional information about the individuals themselves. Any additional data would be added to the statistical model. IFS would like add extra characteristics of the hospitals, as these characteristics might affect the hospital choice of mothers.

(2) Episode level data on NHS funded elective hip replacements will be used to estimate a statistical model of hospital choice. The model will be extended to take into account observed and unobserved sources of patient heterogeneity (differences in preferences).
As it is expected that clinical need will be a crucial input into individual patient decision making, the HES and PROMS datasets will be linked. This linkage will enable the production of a model that takes patient need into account and therefore provide more accurate analysis and predictions. PROMs data are also required over this period in order to understand how the clinical benefits following joint replacement surgery has changed over time, particularly in light of the independent sector reforms. This is essential for an analysis which estimates the implications for population health following the huge increases in the volume of joint replacements observed in the ten years prior (i.e. IFS can examine whether the clinical benefit for patients has increased or decreased as a result of greater availability of hip and knee replacements. This will help to analyse whether the independent sector reforms have increased the welfare of NHS patients).

(3) IFS will calculate the percentage of patients receiving a PCI within 1 day and within 30 days of a first admission for an AMI, and compare these rates to those in the United States. IFS will also calculate these rates by region and PCT/CCG. These data will not contain rates based on fewer than 50 patients to ensure that the data are not disclosive. It will be necessary to calculate rates back to 2000 to understand whether the countries have been converging or diverging overtime as the treatment became more widespread in the early 2000s. IFS require the most recent data as the preferred journal requires data from the past 5 years to be included.

(4) IFS will model the relationship between the total number of NHS funded elective hip replacements in a middle super output area in a given year, and the introduction of Independent Sector Providers. IFS will compare the same area over time, and compare across areas by distance to the nearest independent sector provider offering hip replacements, relative to the nearest Acute Trust providing hip replacements. Data will be combined with publicly available area level and GP practice characteristics, in order to examine variation in outcomes or behaviour, or to control for potentially confounding factors at the area level. Publicly available area level data includes measures of population size and levels of deprivation. These variables are used in area-level regressions as control variables. This includes population data (available from the Office for National Statistics and local deprivation scores made available for public use by the Department for Communities and Local Government). IFS require data back until at least 2000, as the policy to increase and formalise the role of the Independent Sector began in 2003, and the ability to study a comparison group is essential to accurately identify the impact of the policy.

(5) IFS will examine the age profile of English hospital spending across the period between 1997/98 and 2013/14. Using the Health Resource Group (HRG) variable in the inpatient HES dataset, IFS can allocate costs for all inpatient activities to different age groups. Using publically available data on the English population, the average spending per individuals of a given age can be derived. IFS will then examine how this develops over time, providing evidence on whether average spending for individuals of a given age has changed over time (i.e. is the average spend for a 70 year old male in 1997/98 different to a 70 year old male in 2013/14).
The pseudonymised HES indicator will be used to track the use of hospital care for a random sample of individuals in each year of the data. This will allow an estimate of total healthcare expenditure for individuals over the entire period, providing a measure of “lifetime medical spending” for older individuals. It also allows IFS to examine the correlation between health spending in one year and another (i.e. does health spending in one year predict health spending in the next, or five years later etc).
Individuals who die in hospital are recorded in HES (through the discharge method variable). For individuals who die in hospital, IFS can examine the amount (and cost) of hospital care received in the final year(s) of life. In this way, IFS can estimate the amount and the share of hospital expenditures that are incurred in the final year of life. These estimates can then be compared to the findings of other researchers who are conducting a comparable analysis on similar data in other countries such as the USA and other European countries (Note: IFS will not combine the data with these other researchers, but only examine regression coefficients and the findings of this research).
IFS require data back to 1997/98 to provide the longest time series possible over which you can track individuals using the HES identifier. This will (i) provide the largest history for individuals (and therefore acts as the best proxy for lifetime use of the service) and (ii) provides a significant period of time over which to examine how the distribution of spending across ages has developed (i.e. IFS can examine whether the average 70 year old in 2013/14 uses more healthcare than a 70 year old in 1997/98).

(6) IFS will model the impact of rapid immigration on the demand for accident and emergency services by comparing the change in (i) inpatient admittances for ambulatory care sensitive (ACS) conditions and (ii) visits to A+E, across local authorities with different changes in the concentration of foreign born residents (population data at the local authority level is sourced from the publicly available UK Labour Force Survey). Admittances for ACS conditions are derived from OPCS-4 codes. A similar exercise will be conducted with admittances for maternity patients, comparing the number of birth episodes recorded by inpatient HES across these regions. IFS will then also examine the number of 30 day readmissions for newborn children across these areas, using the pseudonymised HES identifier, to examine whether the quality of maternity care has deteriorated in an observable way in areas where the population has rapidly grown. This will provide evidence on whether NHS trusts adapts quickly to changes in the size and the characteristics of the population which they treat. HES A&E data is required for the most recent period of time in order to understand the use of the service during a period which has witnessed significant changes in the size, and composition, of the English population. This provides sufficient variation in the data to attempt to estimate causal impacts of population change on demand for, and quality of, A&E services.

(7) The same personnel who currently process data for projects involving all HES records will create a dataset that contains only admissions for individuals under the age of 30. The dataset will be placed in a separate secure area for the project team to use, so that they are able to access only the data needed for the project.
To investigate the relationship between Sure Start and hospital admissions, IFS will merge information on the location of Sure Start Centres into HES using LSOA identifiers. IFS will then test whether cohorts exposed to Sure Start (both overall and accounting for intensity of exposure) are less likely to experience hospitalisations and outpatient visits (all-cause and cause-specific) and A&E admissions (from 2007-08 onwards). The two sets of treatment and control groups will be compared: those who lived at ages 0-4 (i) in areas that implemented Sure Start earlier vs. later and (ii) in areas that experienced larger vs. smaller expansions of Sure Start.
To understand the roll out of Sure Start, IFS will examine the determinants of the timing (the year of opening of the first Sure Start Centre in a given Local Authority (LA)) and the intensity (the number of Sure Start Centres in a given LA per year) of the rollout. This will include pre-programme levels and trends in hospitalisations and outpatient visits (all-causes and cause-specific) among the determinants.

(8) The same team who currently process data for projects involving all HES records will create a dataset that contains only drug related admissions.
Episode level data on hospital admissions will be used to compute the frequency of drug-related hospital admissions. This will focus on cannabis related hospital admissions as well as other drug-related and alcohol-related admissions, and will be computed by time period (monthly and yearly) and region (especially TV region equivalents, the regional level at which the tobacco sales data are available) , and in relation to demographic characteristics (such as age group and gender). The output of the analysis will be aggregate data (by region, time period, and for demographic groups), with small numbers suppressed in line with HES analysis guide. This will then be compared with the incidence of cannabis-related hospital admissions to estimates of cannabis market size, which are constructed from other data sources (especially sales data for tobacco-related products). Overall, this will allow IFS to compare market-size estimates to admission based estimates of heavy drug consumption for cannabis, as well as other drugs and alcohol, and to test the relationship between these variables across regions and over time. As the sample sizes are likely to be small, IFS will ensure that only aggregate numbers are reported and any small numbers are suppressed.

(9) IFS will use episode level data to compare 30-day in-hospital mortality rates of patients treated by different consultants following admission to an NHS hospital for an AMI or stroke. Admittances for AMI and stroke are derived from ICD-10 diagnosis codes contained in HES. Consultants are assigned to patients in HES using the anonymised consultant ID (variable ‘pconsult’). Patients who die in hospital are recorded in inpatient HES (through the discharge method variable). Anonymised patient IDs will be used to examine whether patients who are discharged but then readmitted during the 30 day period following the initial admission die in a subsequent hospital spell.
The analysis requires the construction of detailed control variables to account for differences in the underlying health of patients treated by different consultants and hospitals. Failing to account for these differences will lead to inaccurate estimates of the effects that each consultant has on patient outcomes. Detailed measures of health conditions and past hospital use are therefore essential for this analysis. IFS will derive a range of clinical indicators using the ICD-10 diagnosis codes in HES, and use these to create the Charlson Index to capture patient morbidity. Using data from 1997/98 – 2014/15, IFS will use the anonymised patient identifier to track patient inpatient admissions and outpatient attendances over time in order to construct detailed histories of patient hospital use. Using the Health Resource Group (HRG) variable in the inpatient HES dataset, IFS can allocate costs for each of these activities to summarise past hospital use. IFS will also create a variable for each year which indicates whether a patient has been treated for a heart attack or stroke in a previous year. Previous research has shown that a major determinant in survival following a heart attack is the amount of time that elapses between onset and treatment, and the distance that patients need to travel to reach a hospital for treatment. This will be addressed by examining the distance between the Lower Super Output Area of patient residence and the hospital in which they are treatment. In addition, for the period 2007/08 – 2014/15, IFS will use the Accident and Emergency data to examine whether the onset occurred at home (variable ‘aeincloctype’) and the time that elapsed between arrival at hospital and admission (variable ‘tretdur’).
In order to separately estimate the impact of consultants from the hospitals in which they work, the analysis needs to control for differences in the types of patients treated by different hospitals. It also requires the observation of consultants working in different hospitals over time. IFS will address the first point by combining publicly available aggregated geographical data relating to the socio-economic status and population health to summarise the characteristics of the patient population served by each hospital. Inpatient HES data will also be used to create other indicators of patient health and quality of local primary care (e.g. the admission rates for ACS conditions in the local area), and the quality of other care provided in the hospital (e.g. hospital level readmission rates for elective hip replacements). In addition, for the period 2007/08 – 2014/15, Accident and Emergency data can be used to separately analyse the outcomes for patients who arrived at the hospital in an ambulance (contained in variable ‘aearrivalmode’). This would allow analysis on a subset of patients for which it is certain that patient did not choose the hospital in which they were treated, and therefore rules out matching of (otherwise unobservably sicker) patients to hospitals which could potentially bias results.
The second point is addressed by following consultants across hospitals over time, through the use of anonymised consultant team variable (‘pconsult’). This allows a comparison of patient outcomes when treated by the same consultant but in a different setting.
IFS require inpatient and outpatient data back to 1997/98 for two reasons. First, the analysis relies on consultants moving across hospitals over time. Using the longest available period of data will capture substantially more movement in staff across hospitals, and will maximise the sample of patients whose outcomes can be studied. This will increase the precision of the estimates. Second, the panel element of the data will be used to construct detailed histories of hospital use for patients. This will improve the accuracy of the analysis by controlling for a broad range of factors in the underlying health of patients. IFS requires A&E data back to 2007/08 to supplement the analysis of inpatient and outpatient data. Using the full period of available data will maximise the number of patients for which full information on care received from arrival at hospital to discharge is available. Information on the use of an ambulance will provide a subset of patients who we can reasonably assume have no choice over the hospital they attend. IFS will the conduct the analysis for the period between 2007/08 and 2014/15 both with and without use of the additional information of the method of transport to examine whether patients who do not use ambulances selectively sort into particular hospitals. The analysis can then be extended to earlier years (prior to A&E data availability) with a better understanding of whether patient sorting between hospitals occurs.
The analysis on the role of independent sector providers within the NHS requires inpatient HES data up to 2013-14. When examining the impact of these reforms, it is essential to understand whether the trends seen between 2000-1 and 2010-11 continue between 2010-11 and 2013-14. This is a period of time in which (i) NHS funding was relatively restrained and (ii) the wider economy was recovering from a large recession in the preceding years. As a result, to evaluate the impact of the reform on patient health and the quality of NHS services provided, it is crucial to examine the longer term impacts of the reforms. For the work on heart attack treatment, IFS intend to submit this work to the Journal of the American Medical Association, which has a strong preference for work that covers the past 5 years.
Importantly, in all cases the ability to conduct research on the most recent years of data is crucial for the analysis to be both timely and relevant. This maximises the impact of this research on feeding into current policy debates such as the extent to which private providers are used within the NHS, and how the NHS has responded to the challenges posed by a growing and ageing population.
All outputs from each project will contain only aggregate outputs with small numbers suppressed. IFS report aggregate summary statistics (i.e. total number of women giving birth in NHS hospitals in 2010/11) and regression coefficients from large-sample regressions. No record level data will be shared with third parties.
Episode level data on NHS funded elective hip replacements will be used to estimate a statistical model of hospital choice. The model will be extended to take into account observed and unobserved sources of patient heterogeneity (differences in preferences).

Objective: (1) To understand whether and how patients exercise choice when there is no entry of private providers: the case of maternity. The aim is to address three questions: (i), are patients able to make a choice when there is very limited spare capacity in the system? (ii), if patients are able to exercise choice, what types of patients are able and willing to exert choice? (iii) do women use experience from their earlier maternities when making decisions about where and when to give birth to subsequent children?

(2) To produce a model of choice that can be used to simulate and evaluate potential future policies. The focus will be on how potential policies affect where different types of patient (by age, location, or area level deprivation) are treated.

(3) Comparing health care expenditure and activity in the US and England, using the specific case study of percutaneous coronary intervention (PCI) treatment for acute myocardial infarction (AMI) patients aged 65 and over.

(4) The objectives of this project are (i) to understand the impact of the introduction and expansion of the role of independent sector providers on demand for NHS-funded joint replacements and (ii) to assess how this impact varies across England, and the area level deprivation.

(5) The objectives of this project are (i) to produce profiles of public-funded medical expenditure in England over the life cycle (and examine how this evolves over time), (ii) examine correlations in the concentration of medical spending over time (i.e. how much does spending on healthcare in a given year determine the amount of healthcare received in the future), and (iii) examine the share of medical spending attributed to patients in the last year of life.

(6) To investigate how the demand for, and quality of NHS services have changed in areas where population has experienced rapid changes. In particular, IFS will examine whether areas with a high number or concentration of residents who are foreign born experience greater demand for two types of NHS services: (i) Accident and Emergency care and (ii) maternity services.

(7) To estimate the health effects of Sure Start, a large national programme to improve early childhood development and integrate health, education, childcare, social care, and other support services to better serve families. The HES data will be used to: (i) investigate whether access to Sure Start services between birth and age 4 reduced all-cause and cause-specific hospitalisations and outpatient visits; (ii) understand the rollout of the Sure Start programme. This project can be completed with existing data.

(8) To estimate the frequency of drug-related hospital admissions, focusing on cannabis-related hospital admissions, as well as admissions related to other drugs and alcohol, by region (especially TV region equivalents) and period (yearly and monthly), and in relation to demographic characteristics (such as age group and gender). These figures will then be compared to region and period specific estimates of the market size for cannabis, based on other data sources (especially sales data for tobacco-related products). This project can be completed with existing data.

(9) To examine the variation in 30-day mortality rates of patient who are treated for AMI or stroke across different consultants and different hospitals. The focus will be to quantify the extent to which different consultants determine the probability of survival for patients, after taking into account the different characteristics of patients treated by different consultants, and the facilities available to consultants in each NHS hospital. This project requires the pconsult variable. IFS currently hold this variable for 2010/11 onwards, which came as part of the standard extract. This variable is required from 2003/04 to 2009/10.

Benefits: The existing work using HES data has been used to inform policy maker Monitor, NHS England, the Department of Health, the Cabinet Office and representatives from PCTs. As noted by the enclosed letter from the Department of Health, academic work helps to understand the impacts of former policy and how to improve the existing health and social care system. Examples of previous outputs, and the steps IFS have taken to disseminate the findings to policymakers, include:

•The research report “Choosing the place of care” was published in 2012. A summary presentation has since had 9,505 views online. After the report was published, representatives from Monitor, the Department of Health, and NHS England met to discuss the policy implications of these findings. In the months that followed, economists from the cabinet later requested a meeting to ask for advice on NHS competition.
•The research report “Public pay and private provision” was published in 2013. The accompanying presentation has now been viewed 16,342 times online. The results were presented at the Nuffield Trust’s Competition for Care conference in May 2013, which also included speakers from the Competition and Cooperation Panel, Monitor, NHS England and the NHS Confederation. The audience included both national policy makers and local commissioners. The results have also been presented to a meeting of Conservative Health at the House of Commons in July 2013.
•‘Policing Cannabis and drug related hospital admissions: Evidence from administrative records’, an article in the Journal of Public Economics (released April 2014). The Journal of Public Economics is a highly respected peer-reviewed economic journal. The article has 7 citations thus far.
• IFS have invited representatives from the Department of Health, NHS England, and Monitor to two workshops on academic findings on health care in 2013 and 2015, which they have chosen to attend. This indicates that academic work in general, and this work specifically, is valuable to them. Following the 2015 conference, IFS were invited to present the results to the Department of Health, further indicating the importance of this research. Following discussions with DH delegates, IFS are planning to modify their research (distinguishing between spending on elective and emergency care), as it was suggested that this could be useful to the Department during the upcoming spending review.
Future projects will have a range of direct benefits to health and social care over the coming years:

(1) Results from this project will improve the understanding of how and why patients make choices, and more specifically, how women respond to the quality of maternity care. This will benefit the health and social care system in two ways. First, it will assist policy makers in deciding how effective patient choice is in promoting competition between hospitals and therefore driving up standards. Second, it will inform Acute Trusts about how women make choices about where to give birth, and the potential financial consequences for Trusts who lose patients as a result of providing poor quality care. These benefits will be realised over the 3 year project.

(2) The model of hospital choice can be used to model how patients respond to potential policies, such as the reorganization of hospital services, or hospital mergers. The focus on equity is in line with NHS England principles of promoting equality and equity in provision, and the objectives of the Department of Health (see attached letter). IFS will liaise with the Department of Health to understand how the model could be more useful to them. Again, these benefits will accrue over the next three years.

(3) The government has previously used cross-country comparisons to assess the effectiveness of the health care system. The results will aid such comparisons. The results will also provide information that can be used to evaluate the implementation of NICE guidance with respect to the treatment of heart attack patients between 2008 and 2010, which recommended the use of Percutaneous Coronary Intervention for certain patients.

(4) Policies of the previous two parliaments have increased the role of the private sector in delivering NHS funded care, yet there is very little evidence on the impact of these providers on the impact for NHS funded care. This research will help inform policy makers of the potential effects on patient demand, health care supply, the financial implications for NHS providers, and the equity of provision if the role of these providers is expanded in the future. These issues are of importance to the Department of Health, Monitor, the Cabinet Office and NHS England. Thus far, this has been demonstrated by both the Cabinet Office and NHS England requesting updates to this work, indicating that this work has the potential to feed into policy-making in the short to medium term. A working paper version of this research was warmly received by a number of representatives from Monitor, including Chris Pike (Economics Director, Cooperation and Competition at Monitor), when presented in September 2013.

(5) Understanding how population health care needs are likely to change is important for both national policy makers and local commissioners, particularly given tighter NHS budgets. The Department of Health has already shown interest in this work, part of which IFS have been invited to present to the Department in June. The aim is that the findings will also feed into initiatives such as the Better Care Fund and help to achieve efficiency savings set out by the QIPP initiative and NHS England’s Five Year Forward View. This research is supported by the Department of Health, who acknowledged its vital importance and provided supporting evidence for IFS funding applications to the Health Foundation. Representatives from the NHS England Strategy Group have also indicated to IFS the importance of such work in informing future policy when meeting to discuss the work.

(6) Changes in the volume and composition of the population have important implications for the quantity and type of health services demanded by patients. This work will show how rapid population change can affect demand for services, and therefore help CCGs and Acute Trusts plan for the future. This could be in terms of how to organise primary and secondary care services or how to determine future staffing levels. The focus on A&E services should be particularly useful, given the difficulties experienced by A&E departments this past winter. These benefits will accrue over the next 3 years.

(7) The analysis of Sure Start will provide a detailed and thorough cost-benefit analysis of the programme. Sure Start was funded at £1.8 billion in 2010-11 and accounted for a third of government expenditure on early years programmes. Funding per eligible child fluctuated over time but averaged about £6,500 per year. This reflects a considerable government investment in early years interventions, and it is important to understand whether this intervention provided value for money by improving subsequent outcomes.
One of the major rationales for Sure Start is the evidence from other countries that early intervention is more cost-effective than treatment for poor child health. There has been a renewed focus on early intervention in the UK; for example, a 2012 report by the Chief Medical Officer (CMO) called urgently for more investment in early years to prevent poor outcomes later in life. Similarly, the NHS Five-Year Forward View (2014) called for a ‘radical upgrade in prevention.’ The government has made explicit plans to deliver part of this early intervention through Sure Start; for example, the 2009 child health strategy ‘Healthy lives, brighter futures’ envisages a strengthened role for Children's Centres in improving children's health and supporting parents from pregnancy onwards.
Poor child health generates substantial costs for children, their families and the Health and Social Care System . Research by Action for Children and the New Economics Foundation suggests that, over a 20-year period, preventable health and social outcomes faced by children and young people will cost £4 trillion (‘Backing the Future’, 2009). Even the short-term costs imposed are considerable; for example, the short-term hospital costs of severe unintentional injuries to children are estimated at up to £87 million per year (CMO’s Annual Report 2012, Chapter 3, p. 8). Potential long-run costs could be in excess of £2 billion (ibid). Using HES to understand whether Sure Start is an effective way to reduce the rate of these hospitalisations is a concrete example of how IFS hope to add value to the health and social care system. Understanding the role of community based health services is vital in designing new models of care that both deliver better value to the patient and their families, and help the NHS to continue to deliver high quality care with constrained resources.
Given the potential importance of this work to the health and social care system, IFS are strongly committed to reaching out to policymakers to disseminate the findings. To do so, IFS will produce written work targeted at policymakers, including a non-technical report describing their methodology and their findings. This will be freely available on the Institute for Fiscal Studies website. IFS will also provide support in communicating our results, including through a press release; a brief observation note highlighting key findings; and a launch event which will be open to policymakers and the media. This will take place in February 2017 (the end of the project and release of the report).
In addition, IFS plan to reach out directly to key policymakers within the health and social care system. IFS will approach strategists at NHS England and Monitor to discuss the results of their cost-benefit analysis of early intervention. This will give them rigorously-researched information on the effectiveness of early interventions, which they can use to inform and support future strategies. IFS also plan to communicate with health policy organisations, such as the King’s Fund.
In addition, this work will have wider impacts beyond the health and social care system. IFS are already working closely with policymakers within the Department for Education as well as practitioners from the early childhood development field. IFS' advisory group includes Leon Feinstein (Early Intervention Foundation), Kathy Sylva (Oxford University Department of Education), Joanne Pearmain (4Children), Richard Blundell (University College London and IFS), and Margaret Leopold (Department for Education). IFS have the strong support of this advisory group in maximising their policy impact, including their assistance in disseminating the results widely through their networks.
Finally, IFS have existing links with politicians from the all-party parliamentary group 1001 Critical Days (Tim Loughton MP chair) and the Foundation Years Information and Research group (Frank Field MP chair). These groups have already expressed interest in the results. Once the report is published in March 2017, IFS will meet with them to discuss the results and the policy implications of their findings. The findings will inform the recommendations of these groups and will provide a stronger evidence base for early intervention in the UK and ensure value for money is delivered.

(8) Quantifying the market size for cannabis is important given vigorous policy debates about how to intervene in this market. A body of evidence across disciplines has established significant private and social costs associated with the market for cannabis. Private costs borne by users include longer-term impacts on health from prolonged and heavy use [Fergusson and Horwood 1997, Hall and Degenhardt 2009, Marshall et al. 2011], as well as a potentially increased propensity to use other illicit substances [van Ours 2003, Kelly and Rasul 2014]. The social costs of the cannabis market arising through the health systems are substantial. For example, Public Health England estimates that drug misuse costs the NHS in England £488m annually (Public Health England 2013). Better understanding of the market size of cannabis can therefore bring measurable benefits to health and social care. The statistics based on the HES allow to compare cannabis market size estimates (by TV region equivalent and time period) based on sales data from tobacco-related products to hospital admissions for related diagnoses, and will assist policy makers by providing better understanding of how these measures are related. In particular, better understanding of the market size for cannabis can inform policy makers in the area of health and social care by providing important information about the size of the cannabis market, including as input into cost-benefit analysis (CBA).
NHS Health Scotland has made extensive use of alcohol sales data to monitor and evaluate Scotland’s alcohol strategy (NHS Health Scotland 2016), and concluded that sales data can contribute to ensuring that “consumption or related harm is spotted early”, and identified as an area for future research the relationship between “consumption [of alcohol] and harm within Scotland and the rest of the UK” (NHS Health Scotland 2016). The proposed project provides evidence on such a question in the context of drug use, and will assist policy-makers by providing insight into whether such an approach could be used for illicit substances such as cannabis, and develops a new method for doing so.
We will write a policy-focused summary of our results aimed at a practitioner’s audience and the general public, using widely read dissemination websites such as “VoxEU” (http://voxeu.org/content/topics/health-economics) or “The Conversation” (http://theconversation.com/uk/health). For example, previous policy-relevant research summaries by the researchers on VoxEU have been accessed more than 20,000 times. Our links with a prominent professor (University of Essex), who has worked and advised widely on topics related to health and risky behaviours, will also help to disseminate findings to a policy-maker audience. We will distribute our findings to our links and contacts in the NHS. The research paper will be available freely on our website.
These benefits will accrue of the next three years as the paper is prepared, as well as subsequently when the results are available in the publication.

NHS Health Scotland (2016): “Monitoring and Evaluating Scotland’s Alcohol Strategy,” Final Annual Report, March 2016.
Public Health England (2013): “Alcohol and drugs prevention, treatment and recovery: why invest?,” PHE publications gateway number: 2013-190.

(9) Consultants play a crucial role in the delivery of NHS healthcare. However, little is understood about the extent to which patient outcomes depend on the individual consultant who is responsible for their care. This project will provide evidence on the distribution of consultant effects. IFS will meet with NHS England and the Department of Health to discuss the results of their analysis and how the model could be used to guide policy to benefit the health and social care system. The model be used for policy experiments such as “what is the impact of patient survival rates if the 5% of worst performing consultants were replaced by median-performance consultants?”. DH, NHS England or Acute Trusts may use this as a basis for deciding whether some consultants require more training, additional support from the Acute Trust, or should be moved to other positions.

Similar work has been carried out in the past in the United States and these models have been widely adopted by State hospital boards to evaluate provider performance, with positive implications for patient outcomes. By 2006, 47 states used similar models to produce publicly available ‘report cards’ for hospitals. Economic evaluations of the adoption of these hospital performance measures have indicated substantial improvements in the quality of care received by patients at previously poorly-performing providers. For example, researchers found a reduction of a third in the mortality rate of patients undergoing coronary artery bypass graft surgery in New York State between 1991 and 1997 in hospitals which had received a ‘high-mortality’ flag in the previous year (Cutler, Huckman and Landrum, 2004). Similarly, surgeon-specific report cards in Pennsylvania led to significant improvements in risk-adjusted mortality rates in the following years (Kolstad, 2013).

Cutler, D, R. Huckman and M. Landrum. (2004). The role of information in medical markets: An analysis of publicly reported outcomes in cardiac surgery.American Economic Review 94 (2): 342-346
Fergusson, D.M. and l.J. Horwood (1997) “Early Onset of Cannabis Use and Psycho-social Adjustment in Young Adults,” Addiction 92: 279-96.
Hall, W and l. Degenhardt (2009) “Adverse Health Effects of Non-medical Cannabis Use,” Lancet 374: 1383-91.
Kelly, E. and I. Rasul (2014) “Policing Cannabis and Drug Related Hospital Admissions: Evidence from Administrative Records,” Journal of Public Economics 112: 89-114.
Kolstad, J., (2013). Information and Quality When Motivation is Intrinsic: Evidence from Surgeon Report Cards, American Economic Review 2013, 103(7): 2875–2910
Marshall, K.S., l. Gowing and R. Ali (2011) “Pharmacotherapies for Cannabis Withdrawal,” Cochrane Database for Systematic Reviews 1: CD008940.
Van Ours, J. (2003) “Is Cannabis a Stepping-stone for Cocaine?,” Journal of Health Economics 22: 539-54.

Source: NHS Digital.