Health policy analysis
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Our research methods help policy makers make fairer decisions with better health outcomes.

The problem. Existing analyses focus on a mythical average citizen.

The solution. We develop ways of analysing who gains and loses from health policies.

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Monitoring Fairness in the NHS

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Listen to the latest podcast (Jan 2020) from Professor Tim Doran - Looking beyond Horizons at the North South Divide, part of the series 'The Story of Things' hosted by the University of York in partnership with the York Festival of Ideas.

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Really pleased to be leading one of these projects! We will look at how health and place are linked, particularly around:
- disaggregating health data
- the definition and boundaries of 'place' in health geography
- how health and social fragmentation are linked https://twitter.com/HealthFdn/status/1245614458434498560

HEPU is a joint initiative between @CHEyork, @unimaecon and @MalawiGovt. This was made possible through the @thanzilaonse project. HEPU Policy lab has become a one stop centre for health policy engagement thereby reducing the time & cost of doing policy engagement in ECSA region.

@UoBrisHEB @CHEyork @UoBrisHEB @HealthEconLANCS @hergbrunel @HealthEcon_MCR @HealthEcon_Will @HEU_unimelb anything you HE superheroes can do to help @trishgreenhalgh ? https://twitter.com/trishgreenhalgh/status/1242684284365950976

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2020

Publication details

Journal Medical Decision Making
Date Accepted/In press - 1 Oct 2019
Date E-pub ahead of print (current) - 15 Feb 2020
Number of pages 13
Early online date 15/02/20
Original language English

Abstract

Background. A common aim of health expenditure is to reduce unfair inequalities in health. Although previous research has attempted to estimate the total health effects of changes in health expenditure, little is known about how changes affect different groups in the population. Methods. We propose a general framework for disaggregating the total health effects of changes in health expenditure by social groups. This can be performed indirectly when the estimate of the total health effect has first been disaggregated by a secondary factor (e.g., disease area) that can be linked to social characteristics. This is illustrated with an application to the English National Health Service. Evidence on the health effects of expenditure across 23 disease areas is combined with data on the distribution of disease-specific hospital utilization by age, sex, and area-level deprivation. Results. We find that the health effects from NHS expenditure changes are produced largely through disease areas in which individuals from more deprived areas account for a large share of health care utilization, namely, respiratory and neurologic disease and mental health. We estimate that 26% of the total health effect from a change in expenditure would accrue to the fifth of the population living in the most deprived areas, compared with 14% to the fifth living in the least deprived areas. Conclusions. Our approach can be useful for evaluating the health inequality impacts of changing health budgets or funding alternative health programs. However, it requires robust estimates of how health expenditure affects health outcomes. Our example analysis also relied on strong assumptions about the relationship between health care utilization and health effects across population groups.

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

Universal or targeted cardiovascular screening? Modelling study using a sector-specific distributional cost effectiveness analysis

Collins, B., Kypridemos, C., Cookson, R., Parvulescu, P., McHale, P., Guzman-Castillo, M., Bandosz, P., Bromley, H., Capewell, S. & O'Flaherty, M., 1 Jan 2020

Article in Preventive medicine

Publication details

Journal Preventive medicine
Date Accepted/In press - 23 Oct 2019
Date E-pub ahead of print - 31 Oct 2019
Date Published (current) - 1 Jan 2020
Volume 130
Early online date 31/10/19
Original language English

Abstract

Distributional cost effectiveness analysis is a new method that can help to redesign prevention programmes by explicitly modelling the distribution of health opportunity costs as well as the distribution of health benefits. Previously we modelled cardiovascular disease (CVD) screening audit data from Liverpool, UK to see if the city could redesign its cardiovascular screening programme to enhance its cost effectiveness and equity. Building on this previous analysis, we explicitly examined the distribution of health opportunity costs and we looked at new redesign options co-designed with stakeholders. We simulated four plausible scenarios: a) no CVD screening, b) ‘current’ basic universal CVD screening as currently implemented, c) enhanced universal CVD screening with ‘increased’ population-wide delivery, and d) ‘universal plus targeted’ with top-up delivery to the most deprived fifth. We also compared assumptions around whether displaced health spend would come from programmes that might benefit the poor more and how much health these programmes would generate. The main outcomes were net health benefit and change in the slope index of inequality (SII) in QALYs per 100,000 person years. ‘Universal plus targeted’ dominated ‘increased’ and ‘current’ and also reduced health inequality by −0.65 QALYs per 100,000 person years. Results are highly sensitive to assumptions about opportunity costs and, in particular, whether funding comes from health care or local government budgets. By analysing who loses as well as who gains from expenditure decisions, distributional cost effectiveness analysis can help decision makers to redesign prevention programmes in ways that improve health and reduce health inequality.

Bibliographical note

© 2019 The Authors.

2019

Publication details

Journal Health services research
Date Submitted - 12 May 2018
Date Accepted/In press - 22 Aug 2019
Date E-pub ahead of print - 9 Oct 2019
Date Published (current) - 19 Nov 2019
Volume 54
Number of pages 10
Pages (from-to) 1316-1325
Early online date 9/10/19
Original language English

Abstract

Objective: To investigate whether continuity of care in family practice reduces unplanned hospital use for people with serious mental illness (SMI). Data Sources Linked administrative data on family practice and hospital utilization by people with SMI in England, 2007-2014. Study Design: This observational cohort study used discrete-time survival analysis to investigate the relationship between continuity of care in family practice and unplanned hospital use: emergency department (ED) presentations, and unplanned admissions for SMI and ambulatory care-sensitive conditions (ACSC). The analysis distinguishes between relational continuity and management/ informational continuity (as captured by care plans) and accounts for unobserved confounding by examining deviation from long-term averages. Data Collection/Extraction Methods: Individual-level family practice administrative data linked to hospital administrative data. Principal Findings: Higher relational continuity was associated with 8-11 percent lower risk of ED presentation and 23-27 percent lower risk of ACSC admissions. Care plans were associated with 29 percent lower risk of ED presentation, 39 percent lower risk of SMI admissions, and 32 percent lower risk of ACSC admissions. Conclusions: Family practice continuity of care can reduce unplanned hospital use for physical and mental health of people with SMI.

Bibliographical note

© 2019 The Authors

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EQUIPOL is supported by the University of York, the Wellcome Trust (Grant No. 205427/Z/16/Z) and the NIHR (SRF-2013-06-015).

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