Brendan Collins

Return on investment of public health interventions: a systematic review

Background Public sector austerity measures in many high-income countries mean that public health budgets are reducing year on year. To help inform the potential impact of these proposed disinvestments in public health, we set out to determine the return on investment (ROI) from a range of existing public health interventions. Methods We conducted systematic searches on all relevant databases (including MEDLINE; EMBASE; CINAHL; AMED; PubMed, Cochrane and Scopus) to identify studies that calculated a ROI or cost-benefit ratio (CBR) for public health interventions in high-income countries. Results We identified 2957 titles, and included 52 studies. The median ROI for public health interventions was 14.3 to 1, and median CBR was 8.3. The median ROI for all 29 local public health interventions was 4.1 to 1, and median CBR was 10.3. Even larger benefits were reported in 28 studies analysing nationwide public health interventions; the median ROI was 27.2, and median CBR was 17.5. Conclusions This systematic review suggests that local and national public health interventions are highly cost-saving. Cuts to public health budgets in high income countries therefore represent a false economy, and are likely to generate billions of pounds of additional costs to health services and the wider economy.

Big Data and Health Economics: Strengths, Weaknesses, Opportunities and Threats

Abstract‘Big data’ is the collective name for the increasing capacity of information systems to collect and store large volumes of data, which are often unstructured and time stamped, and to analyse these data by using regression and other statistical techniques. This is a review of the potential applications of big data and health economics, using a SWOT (strengths, weaknesses, opportunities, threats) approach. In health economics, large pseudonymized databases, such as the planned care.data programme in the UK, have the potential to increase understanding of how drugs work in the real world, taking into account adherence, co-morbidities, interactions and side effects. This ‘real-world evidence’ has applications in individualized medicine. More routine and larger-scale cost and outcomes data collection will make health economic analyses more disease specific and population specific but may require new skill sets. There is potential for biomonitoring and lifestyle data to inform health economic analyses and public health policy.

Using a Survey to Estimate Health Expectancy and Quality-Adjusted Life Expectancy to Assess Inequalities in Health and Quality of Life

BACKGROUND There has been a policy debate in the United Kingdom about moving beyond traditional measures of life expectancy and economic output to developing more meaningful ways of measuring national well-being. OBJECTIVE To test whether quality adjusted life expectancy (QALE) was a useful indicator of health inequalities. METHODS EuroQol five-dimensional questionnaire data from a well-being survey was combined with actuarial life expectancy (LE) data to estimate healthy LE (HLE), that is, years of life lived in good health, and QALE, that is, quality-adjusted life-years (QALYs) lived for Wirral, a borough in the north west of England. RESULTS The gap between Wirral and the most deprived areas was 4.45 years for LE, 5.34 for QALE, and 7.55 for HLE. The gap in QALE was 20% greater than the gap in LE, while the gap in HLE was 70% greater. CONCLUSIONS The fact that the QALE gap value lies between the HLE value and the LE value suggests that QALE is a more sensitive indicator than HLE, as in this study QALE is derived from 243 possible EuroQol five-dimensional questionnaire profiles whereas HLE is based only on whether or not an individual rates his or her health as good, a binary variable. This study discusses how QALE could be a useful indicator for measuring health inequalities in future, especially as cost utility and QALYs are seen as the gold standard used by the National Institute for Health and Clinical Excellence in the United Kingdom to measure outcomes for health interventions in England, and discusses how a monetary valuation of QALYs could be used to put a societal cost on health inequalities.

Modelling the Health Impact of an English Sugary Drinks Duty at National and Local Levels

Background Increasing evidence associates excess refined sugar intakes with obesity, Type 2 diabetes and heart disease. Worryingly, the estimated volume of sugary drinks purchased in the UK has more than doubled between 1975 and 2007, from 510ml to 1140ml per person per week. We aimed to estimate the potential impact of a duty on sugar sweetened beverages (SSBs) at a local level in England, hypothesising that a duty could reduce obesity and related diseases. Methods and Findings We modelled the potential impact of a 20% sugary drinks duty on local authorities in England between 2010 and 2030. We synthesised data obtained from the British National Diet and Nutrition Survey (NDNS), drinks manufacturers, Office for National Statistics, and from previous studies. This produced a modelled population of 41 million adults in 326 lower tier local authorities in England. This analysis suggests that a 20% SSB duty could result in approximately 2,400 fewer diabetes cases, 1,700 fewer stroke and coronary heart disease cases, 400 fewer cancer cases, and gain some 41,000 Quality Adjusted Life Years (QALYs) per year across England. The duty might have the biggest impact in urban areas with young populations. Conclusions This study adds to the growing body of evidence suggesting health benefits for a duty on sugary drinks. It might also usefully provide results at an area level to inform local price interventions in England.

The incidence and implications of cerebral palsy following potentially avoidable obstetric complications: a preliminary burden of disease study

To determine the extent of cerebral palsy attributable to adverse obstetric events, and estimate the lifetime mortality and morbidity expectations of these individuals relative to age‐matched members of the UK general population.

Big Data analysis to improve care for people living with serious illness: The potential to use new emerging technology in palliative care.

We read with interest the editorial by Peter Tanuesputro,1 which describes how Big Data analysis of linked data has the potential to support palliative care by improving identification of patient needs. The editorial described how the use of predictive algorithms (built using routinely collected data) could help to personalise care for people with palliative care needs. However, it is necessary to address the potential barriers that may prevent the full potential of Big Data from being realised.2 Some of these challenges (highlighted in the editorial) include the inadequate capture of certain information, such as nonhospital and mortality data, patient experience information and available familial/social support. In addition to these challenges, we discuss five additional issues that need to be considered in order to make meaningful use of Big Data analysis in palliative care.

Assessing the effectiveness and cost-effectiveness of drug intervention programs: UK case study

ABSTRACT The effectiveness and cost-effectiveness of the UK Drug Interventions Program which directs adult drug-misusing offenders out of crime and into treatment programs was established. Quality-adjusted life year estimates from the UK Drug Treatment Outcomes Research Study were collected and a cost-utility assessment of the Drug Interventions Program was conducted. Cost-utility assessment confirmed that the Drug Interventions Program is both effective and cost-effective with an average net cost saving of £668 (£6,207 including one case of homicide). This study provides evidence that drug intervention programs are cost-effective as they reduce crime, improve quality-of-life and reduce subsequent drug use.

Avoiding Radical Surgery in Elderly Patients With Rectal Cancer Is Cost-effective

BACKGROUND: Radical surgery is associated with significant perioperative mortality in elderly and comorbid populations. Emerging data suggest for patients with a clinical complete response after neoadjuvant chemoradiotherapy that a watch-and-wait approach may provide equivalent survival and oncological outcomes. OBJECTIVE: The purpose of this study was to compare the cost-effectiveness of watch and wait and radical surgery for patients with rectal cancer after a clinical complete response following chemoradiotherapy. DESIGN: Decision analytical modeling and a Markov simulation were used to model long-term costs, quality-adjusted life-years, and cost-effectiveness after watch and wait and radical surgery. Sensitivity analysis was used to investigate the effect of uncertainty in model parameters. SETTINGS: A third-party payer perspective was adopted. PATIENTS: Patients included in the study were a 60-year–old male cohort with no comorbidities, 80-year–old male cohorts with no comorbidities, and 80-year–old male cohorts with significant comorbidities. INTERVENTIONS: Radical surgery and watch-and-wait approaches were studied. MAIN OUTCOME MEASURES: Incremental cost, effectiveness, and cost-effectiveness ratio over the entire lifetime of the hypothetical patient cohorts were measured. RESULTS: Watch and wait was more effective (60-year–old male cohort with no comorbidities = 0.63 quality-adjusted life-years (95% CI, 2.48–3.65 quality-adjusted life-years); 80-year–old male cohort with no comorbidities = 0.56 quality-adjusted life-years (95% CI, 0.52–1.59 quality-adjusted life-years); 80-year–old male cohort with significant comorbidities = 0.72 quality-adjusted life-years (95% CI, 0.34–1.76 quality-adjusted life-years)) and less costly (60-year–old male cohort with no comorbidities =

Promoting health and reducing costs: a role for reform of self-monitoring of blood glucose provision within the National Health Service.

11,332.35 (95% CI,

Estimating the health and economic effects of the proposed US Food and Drug Administration voluntary sodium reformulation: Microsimulation cost-effectiveness analysis

668.50–