Rachel Houten’s journal round-up for 8th July 2019

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.

Credits

Paul Mitchell’s journal round-up for 15th May 2017

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.

Informal care: choice or constraint. Scandinavian Journal of Caring Sciences [PubMed] Published 12th April 2017

The provision of social care in the UK has become a major economic issue, with recent increases in government spending and local authority taxation to help ease the burden on both the health and social care system in the short term. This study examines some of the issues surrounding informal carers (i.e. care of a family member), estimated to be approximately 10% of the UK population. In particular, it focuses on the role of choice and constraints involved with the decision to become a carer. Using a cross-sectional survey for a UK city, choice of caring was explored in terms of responses to care provision provided, asking if it was a free choice initially to provide care, and if there were constraints in terms of duty, lack of others or financial resources for paid care. The analysis focused on how perceived choice in the caring role was associated with socio-demographics and the type of caring role performed, as well as the role of perceived choice in caring and their wellbeing. Out of the 798 respondents to all four questions on caring choice, about 1 in 3 reported an entirely free choice in the decision, with half reporting having a free choice but also a constraint in terms of duty, other available carers or financial resources. Less than 1 in 5 reported not having a free choice. Only carers with bad health or receiving state benefits had an association with a constrained caring role. The more intense the care role was also associated with a more constrained choice. Higher levels of choice were associated with higher levels of wellbeing across measures of happiness, life satisfaction and capability. In multivariable regression analysis, it was found that having a free choice in the initial caring decision resulted in a higher impact on life satisfaction than educational qualifications and home ownership, whilst improved capability of comparable levels to that of home ownership, all else being equal. The authors thus recommend enhanced choice as a way for policy to improve carers wellbeing. Although the authors acknowledge limitations with the study design being cross-sectional and geographically limited to one city, the study shows there is plenty of scope for understanding the determinants of informal caring and consequences for those carers in much greater detail in future national surveys to help address policy in this area in the medium to longer term.

Experienced utility or decision utility for QALY calculation? Both. Public Health Ethics [PhilPapersPublished 6th May 2017

How health states should be valued in population health metrics, like QALYs and DALYs, will not be an unfamiliar topic of discussion for regular readers of this blog. Instead of arguing for decision utility (i.e. accounting for general population preferences for avoiding health states) or experienced utility (i.e. accounting for patient experiences of health states), the authors in this paper argue for a combined approach, reviving a suggestion previously put forward by Lowenstein & Ubel. The authors neatly summarise some of the issues of relying on either decision utility or experienced utility approaches alone and instead argue for better informed decision utility exercises by using deliberative democracy methods where experienced utility in health states are also presented. Unfortunately, there is little detail of how this process might actually work in practice. There are likely to be issues of what patient experiences are presented in such an exercise and how other biases that may influence decision utility responses are controlled for in such an approach. Although I am generally in favour of more deliberative approaches to elicit informed values for resource allocation, I find that this paper makes a convincing case for neither of the utility approaches to valuation, rather than both.

The value of different aspects of person-centred care: a series of discrete choice experiments in people with long-term conditions. BMJ Open [PubMed] Published 26th April 2017

The term “person-centred care” is one which is gaining some prominence in how healthcare is provided. What it means, and how important different aspects of person-centred care are, is explored in this study using discrete choice experiments (DCEs). Through focus groups and drawing from the authors’ own experience in this area, four aspects of person-centred care for self-management of chronic conditions make up the attributes in the DCE across two levels: (i) information (same information for all/personalised information); (ii) situation (little account of current situation/suggestions that fit current situation); (iii) living well (everyone wants the same from life/works with patient for what they want from life); (iv) communication (neutral professional way/friendly professional way). A cost attribute was also attached to the DCE that was given to patient groups with chronic pain and chronic lung disease. The overall findings suggest that person-centred care focused on situation and living well were valued most with personal communication style valued the least. Latent class analysis also suggested that 1 in 5 of those sampled valued personalised information the most. Those with lower earnings were likely to look to reduce the cost attribute the most. The authors conclude that the focus on communication in current clinician training on person-centred care may not be what is of most value to patients. However, I am not entirely convinced by this argument, as it could be that communication was not seen as an issue by the respondents, perhaps somewhat influenced due to the skills clinicians already have obtained in this area. Clearly, these process aspects of care are difficult to develop attributes for in DCEs, and the authors acknowledge that the wording of the “neutral” and “high” levels may have biased responses. I also found that dropping the “negative” third level for each of the attributes unconvincing. It may have proved more difficult to complete than two levels, but it would have shown in much greater depth how much value is attached to the four attributes relative to one another.

Credits

Hidden costs of the recession

In a previous post I considered whether the current Great Recession had been good for your health. Evidence suggests that temporary reductions in income may improve your health for a number of reasons. In part, when I lose my job I may have expectations of finding work again in the short term, my skills may not depreciate in the short term, and I may be able to smooth my consumption with access to credit or savings and do more time-consuming, health-promoting things. But, the longer my spell of unemployment, the less access to health promoting goods I have and the greater the effects of socioeconomic deprivation. A number of studies have remarked on the link between income inequality and poor health (e.g. see here and here).

In the last post, I looked at a cross section of data from the 2011 census. I presented some correlations between the proportion of individuals who were unemployed and the proportion reporting bad health. I, and I am certainly not alone, may argue that myriad other factors could cause this observed relationship. I can’t prove or disprove any hypothesis in the space that this blog permits but I will add the following figure in support of the relationship. Here, I took data from both the 2001 and 2011 censuses for all lower super output areas (LSOAs; geographical areas of approximately 1,500 people) and looked at the relationship between the difference in the proportion unemployed and the difference in the proportion reporting bad health between 2001 and 2011:

change in prop bad health vs change unemployed

Given the long lag between 2001 and 2011, the arguments from the previous post, that this represents changes to structural unemployment rather than short term cyclical unemployment, may still stand. But, for whatever reason, there is a correlation between unemployment and self-reported bad health.

I should mention that the questions about health differed between the two censuses from three options in 2001: ‘good health’, ‘fair health’, or ‘bad health’, compared to five options in 2011: ‘very good health’, ‘good health’, ‘fair health’, ‘bad health’, and ‘very bad health’. I have compared here the percentage reporting the 2001 option ‘bad health’ to the combined ‘bad health’ and ‘very bad health’ option. You may think this is an affront to good data analysis, so to allay your fears I have provided versions of the following two figures that use only 2011 data. You will see that they tell the same story.

The increase to poor health as a result of increased socioeconomic deprivation is costly for a number of reasons. Considering healthcare, direct costs such as hospital admissions for physical and mental health problems may increase, along with the accompanying costs of providing pharmaceuticals and other treatments. One cost that is not well reported in the media is that of unpaid care. One study in the UK estimated the costs of services provided by unpaid carers to be as much as £87 billion per year. Now, those in poor health require care. The following figure shows the relationship between the change in the proportion of people reporting bad health and the change in the proportion of people providing more than 20 hours a week of unpaid care between 2001 and 2011 in each LSOA:

bad health vs unpaid care

bad health vs unpaid care 2011

2011 data only

I am not surprised by this relationship, and I doubt you are either. Then, it should also come as no surprise, given the previous two figures, that when I plot the relationship between the difference in the proportion unemployed and the difference in the proportion providing more than 20 hours unpaid care per week that there is also a strong relationship:

unemployed vs unpaid care

2011 data only

2011 data only

The relationship between health and economic conditions is complicated to say the least. What these data may indicate is that the cost due to increased unemployment may be far more than just reduced growth and output. Unpaid carers often have to leave employment to provide their services. Cutting back on health and social care funding in real terms will only shift the growing burden to individuals in poor areas, where health is worse, rather than to the state.

I would like to point out as a final note, and perhaps one of optimism, that the percentage of people reporting bad health has on average declined between 2001 and 2011. Although this may just be a case of hedonic adaptation…