Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.
Adjusting for inflation and currency changes within health economic studies. Value in Health Published 13th June 2019
The purpose of the paper is to highlight the need for transparency in the reporting of methods of currency conversions and adjustments to costs to take inflation into account, in economic evaluations. It chimes with other recent literature which is less prescriptive in terms of providing methods guidelines and more about advocating the “tell us what you did and why” approach. It reminds me of my very first science lesson in high school where we were eager to get our hands on the experiments yet the teacher (met by much eye-rolling) insisted on the importance of describing the methods of any ‘study’. With space at a premium in academic writing, I know, and I’m likely guilty of, some transparency in assumptions being culled, but papers such as this highlight their necessity.
The authors discuss which inflation measure to base the adjustments on, whether to convert local currencies to US or International dollars, three methods of adjusting for inflation, and what to do when costs from other settings are part of the analysis. With a focus on low- and middle-income countries, and using a hypothetical example, the authors demonstrate that employing three different methods of adjusting for inflation can result in a large range in the final estimates.
The authors acknowledge that it is not a one-size-fits-all approach but favour a ‘mixed approach’ where micro-costing is possible and items can be classified as tradable and non-tradable, as they say this is likely to produce the most accurate estimates. However, a study reliant on previously published costing information would need to follow an alternative approach, of which there are two others detailed in the paper.
In terms of working with data from low- and middle-income countries, I can’t say it is my forté. However, the paper summarises the pros and cons of each of their proposed approaches in a straightforward way. The authors include a table that I think would provide an excellent reference point for anyone considering the best approach for their specific set of circumstances.
An updated systematic review of studies mapping (or cross‑walking) measures of health‑related quality of life to generic preference‑based measures to generate utility value. Applied Health Economics and Health Policy [PubMed] [RePEc] Published 3rd April 2019
This is an update of a review of studies published before 2007, which found 30 studies mapping to generic preference-based measures. This latest paper cites 180 included studies with a total of 233 mapping functions reported. The majority of the mapping functions were to the EQ-5D (147 mapping functions) with the second largest group mapping to the SF-6D (45 mapping functions).
Along with an increase in volume of mapping studies since the last review, there has been a marked increase in the different types of regression methods used, which signals a greater consideration of the distribution of the underlying utility data. Reporting on how well the mapping algorithms predict utility in different sub-groups has also increased.
The authors highlight that although mapping can fill an evidence gap, the uncertainty in the estimates is greater than directly measuring health-related quality of life in prospective studies. The authors signpost to ISPOR guidelines for the reporting of mapping studies and emphasise the need to include measurements of error as well as a plot of predicted versus observed values, to enable the user to understand and incorporate the accuracy of the mapping in their economic evaluations.
As stated by the authors, the results of this review provides a useful resource in terms of a catalogue of mapping studies, however it lacks any quality assessment of the studies (also made clear by the authors), so the choice of which mapping algorithm to use is still ours, and takes some thought. The supplementary Excel file is a great resource to aid the choice as it includes some information about the populations used in the mapping studies alongside the methods, but more studies comparing mapping functions with the same aim against each other would be welcomed.
Investigating the relationship between formal and informal care: an application using panel data for people living together. Health Economics [PubMed] Published 7th June 2019
This paper adds to the literature on informal care by considering co-resident informal care in a UK setting using data from the British Household Panel Survey (BHPS). There has been an increase in the proportion of people receiving non-state provided care in recent years in the UK, and the BHPS also enables the impact of informal care on the use of each of these types of formal care to be explored.
The authors used an instrument for informal care to try to prevent bias due to correlations with other variables such as health. The instrument used for the availability of informal care was the number of adult daughters as it was found to be the most predictive (oh dear, I’ve two sons!). The authors then estimated the impact of informal care on home help, health visitor use, GP visits, and hospital stays.
In this study, informal care was a substitute for both state and non-state home help (with the impact greater for state home help) and complimentary to health visitor use, GP visits, and hospital stays. The authors suggest this may be due to the tasks completed by these different types of service providers and how household tasks are more likely to be undertaken by informal care givers than those more medical in nature. The fact this study considers co-residential care from any household member may explain the stronger substitution effect in this study compared to previous studies looking at informal caregivers living elsewhere as it could be assumed the caregiver residing with the care recipient is more able to provide care.
I find the make-up of households and how that impacts on the need for healthcare resources really interesting, especially as it is generally considered that informal care and the work of charities bolsters the NHS. The results of this study suggest that increases in informal care could generate savings in terms of the need for home help, but an increase in formal care resource use. The reasons for the complimentary relationship between informal care and health visitor, GP, and hospital visits need further exploration.