Health policy analysis
in full colour


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.

Find out more

Monitoring Fairness in the NHS

Latest tweets

A useful guide on ‘How Economic Evaluations Inform Decisions in Health?’ produced by our colleagues @CHEyork & @CLAHRCYH #HEOM to view @ https://t.co/uhdRfuOh3B

Today's @HOPE_UoM seminar will be given by Noemi Kreif from @CHEyork and will focus on new 'big data' methods for evaluating health policies. Details below:

Title: Machine learning in policy evaluation: new tools for causal inference  

Time: 14:00 - 15:30

Venue: Roscoe 1.003

Are you considering a hip, knee or hernia operation?
You can use this @CHEyork website to see how patients of your age and with similar health problems felt after they had their operation
https://t.co/bpNXOSdJnR

Load More...

New resources

2019

Publication details

Journal Psychiatric services (Washington, D.C.)
Date Accepted/In press - 21 Mar 2019
Date E-pub ahead of print (current) - 21 May 2019
Number of pages 7
Early online date 21/05/19
Original language English

Abstract

OBJECTIVE: Although U.K. and international guidelines recommend monotherapy, antipsychotic polypharmacy among patients with serious mental illness is common in clinical practice. However, empirical evidence on its effectiveness is scarce. Therefore, the authors estimated the effectiveness of antipsychotic polypharmacy relative to monotherapy in terms of health care utilization and mortality.

METHODS: Primary care data from Clinical Practice Research Datalink, hospital data from Hospital Episode Statistics, and mortality data from the Office of National Statistics were linked to compile a cohort of patients with serious mental illness in England from 2000 to 2014. The antipsychotic prescribing profile of 17,255 adults who had at least one antipsychotic drug record during the period of observation was constructed from primary care medication records. Survival analysis models were estimated to identify the effect of antipsychotic polypharmacy on the time to first occurrence of each of three outcomes: unplanned hospital admissions (all cause), emergency department (ED) visits, and mortality.

RESULTS: Relative to monotherapy, antipsychotic polypharmacy was not associated with increased risk of unplanned hospital admission (hazard ratio [HR]=1.14; 95% confidence interval [CI]=0.98-1.32), ED visit (HR=0.95; 95% CI=0.80-1.14), or death (HR=1.02; 95% CI=0.76-1.37). Relative to not receiving antipsychotic medication, monotherapy was associated with a reduced hazard of unplanned admissions to the hospital and ED visits, but it had no effect on mortality.

CONCLUSIONS: The study results support current guidelines for antipsychotic monotherapy in routine clinical practice. However, they also suggest that when clinicians have deemed antipsychotic polypharmacy necessary, health care utilization and mortality are not affected.

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.

Publication details

Journal Value in Health
Date Accepted/In press - 18 Mar 2019
Date Published (current) - 17 May 2019
Issue number 5
Volume 22
Number of pages 526
Pages (from-to) 518
Original language English

Abstract

Background
Health inequalities can be partially addressed through the range of treatments funded by health systems. However, whilst health technology assessment agencies assess the overall balance of health benefits and costs, no quantitative assessment of health inequality impact is consistently undertaken.

Methods
The inequality impact of technologies recommended under the NICE single technology appraisal process from 2012-2014 is assessed using an aggregate distributional cost-effectiveness framework. Data on health benefits, costs and patient populations are extracted from the NICE website. Benefits for each technology are distributed to social groups using the observed socioeconomic distribution of hospital utilisation for the targeted disease. Inequality measures and estimates of cost-effectiveness are compared using the health inequality impact plane and combined using social welfare indices.

Results
Twenty-seven interventions are evaluated. 14 interventions are estimated to increase population health and reduce health inequality, eight to reduce population health and increase health inequality, and five to increase health and increase health inequality. Among the latter five, social welfare analysis, using inequality aversion parameters reflecting high concern for inequality, indicated that the health gain outweighs the negative health inequality impact.

Conclusions
The methods proposed offer a way of estimating the health inequality impacts of new health technologies. The methods do not allow for differences in technology-specific utilisation and health benefits, but require less resources and data than conducting full distributional cost-effectiveness analysis. They can provide useful quantitative information to help policy makers consider how far new technologies are likely to reduce or increase health inequalities.

Bibliographical note

© 2019, ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc.

More

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

Image Image Image