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.

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Sam Watson’s journal round-up for 25th February 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.

Democracy does cause growth. Journal of Political Economy [RePEc] Published January 2019

Citizens of a country with a democratic system of government are able to affect change in its governors and influence policy. This threat of voting out the poorly performing from power provides an incentive for the government to legislate in a way that benefits the population. However, democracy is certainly no guarantee of good governance, economic growth, or population health as many events in the last ten years will testify. Similarly, non-democracies can also enact policy that benefits the people. A benevolent dictator is not faced with the same need to satisfy voters and can enact politically challenging but beneficial policies. People often point to China as a key example of this. So there remains the question as to whether democracy per se has any tangible economic or health benefits.

In a past discussion of an article on democratic reform and child health, I concluded that “Democratic reform is neither a sufficient nor necessary condition for improvements in child mortality.” Nevertheless democracy may still be beneficial, on average, given the in-built safeguards against poor leaders. This paper, which has been doing the rounds for years as a working paper, is another examination of the question of the impact of becoming democratic. Principally the article is focused on economic growth, but health and education outcomes feature (very) briefly. The concern I have with the article mentioned at the beginning of this paragraph and with this newly published article are that they do not consider in great detail why democratisation occurred. As much political science work points out, democratic reform can be demanded in poor economic conditions due to poor governance. For these endogenous changes economic growth causes democracy. Whereas in other countries democracy could come about in a more exogenous manner. Lumping them all in together may be misleading.

While the authors of this paper provide pages after pages of different regression specifications, including auto-regressive models and instrumental variables models, I remain unconvinced. For example, the instrument relies on ‘waves’ of transitions: a country is more likely to shift politically if its regional neighbours do, like the Arab Spring. But neither economic nor political conditions in a given country are independent of its neighbours. In somewhat of a rebuttal, Ruiz Pozuelo and other authors conducted a survey to try to identify and separate out those countries which transitioned to democracy endogenously and exogenously (from economic conditions). Their work suggests that the countries that transitioned exogenously did not experience growth benefits. Taken together this shows the importance of theory to guide empirical work, and not the other way round.

Effect of Novartis Access on availability and price of non-communicable disease medicines in Kenya: a cluster-randomised controlled trial. Lancet: Global Health Published February 2019

Access to medicines is one of the key barriers to achieving universal health care. The cost-effectiveness threshold for many low income countries rules out many potentially beneficial medicines. This is in part driven though by the high prices charged by pharmaceutical countries to purchase medicine, which often do not discriminate between purchasers with high and low abilities to pay. Novartis launched a scheme – Novartis Access – to provide access to medicines to low and middle income countries at a price of US$1 per treatment per month. This article presents a cluster randomised trial of this scheme in eight counties of Kenya.

The trial provided access to four treatment counties and used four counties as controls. Individuals selected at random within the counties with non-communicable diseases and pharmacies were the principal units within the counties at which outcomes were analysed. Given the small number of clusters, a covariate-constrained randomisation procedure was used, which generates randomisation that ensures a decent balance of covariates between arms. However, the analysis does not control for the covariates used in the constrained randomisation, which can lead to lower power and incorrect type one error rates. This problem is emphasized by the use of statistical significance to decide on what was and was not affected by the Novartis Access program. While practically all the drugs investigated show an improved availability, only the two with p<0.05 are reported to have improved. Given the very small sample of clusters, this is a tricky distinction to make! Significance aside, the programme appears to have had some success in improving access to diabetes and asthma medication, but not quite as much as hoped. Introductory microeconomics though would show how savings are not all passed on to the consumer.

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Sam Watson’s journal round-up for 12th November 2018

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.

Estimating health opportunity costs in low-income and middle-income countries: a novel approach and evidence from cross-country data. BMJ Global Health. Published November 2017.

The relationship between health care expenditure and population health outcomes is a topic that comes up often on this blog. Understanding how population health changes in response to increases or decreases in the health system budget is a reasonable way to set a cost-effectiveness threshold. Purchasing things above this threshold will, on average, displace activity with greater benefits. But identifying this effect is hard. Commonly papers use some kind of instrumental variable method to try to get at the causal effect with aggregate, say country-level, data. These instruments, though, can be controversial. Years ago I tried to articulate why I thought using socio-economic variables as instruments was inappropriate. I also wrote a short paper a few years ago, which remains unpublished, that used international commodity price indexes as an instrument for health spending in Sub-Saharan Africa, where commodity exports are a big driver of national income. This was rejected from a journal because of the choice of instruments. Commodity prices may well influence other things in the country that can influence population health. And a similar critique could be made of this article here, which uses consumption:investment ratios and military expenditure in neighbouring countries as instruments for national health expenditure in low and middle income countries.

I remain unconvinced by these instruments. The paper doesn’t present validity checks on them, which is forgiveable given medical journal word limitations, but does mean it is hard to assess. In any case, consumption:investment ratios change in line with the general macroeconomy – in an economic downturn this should change (assuming savings = investment) as people switch from consumption to investment. There are a multitude of pathways through which this will affect health. Similarly, neighbouring military expenditure would act by displacing own-country health expenditure towards military expenditure. But for many regions of the world, there has been little conflict between neighbours in recent years. And at the very least there would be a lag on this effect. Indeed, in all the models of health expenditure and population health outcomes I’ve seen, barely a handful take into account dynamic effects.

Now, I don’t mean to let the perfect be the enemy of the good. I would never have suggested this paper should not be published as it is, at the very least, important for the discussion of health care expenditure and cost-effectiveness. But I don’t feel there is strong enough evidence to accept these as causal estimates. I would even be willing to go as far to say that any mechanism that affects health care expenditure is likely to affect population health by some other means, since health expenditure is typically decided in the context of the broader public sector budget. That’s without considering what happens with private expenditure on health.

Strategic Patient Discharge: The Case of Long-Term Care Hospitals. American Economic Review. [RePEcPublished November 2018.

An important contribution of health economics has been to undermine people’s trust that doctors act in their best interest. Perhaps that’s a little facetious, nevertheless there has been ample demonstration that health care providers will often act in their own self-interest. Often this is due to trying to maximise revenue by gaming reimbursement schemes, but also includes things like doctors acting differently near the end of their shift so they can go home on time. So when I describe a particular reimbursement scheme that Medicare in the US uses, I don’t think there’ll be any doubt about the results of this study of it.

In the US, long-term acute care hospitals (LTCHs) specialise in treating patients with chronic care needs who require extended inpatient stays. Medicare reimbursement typically works on a fixed rate for each of many diagnostic related groups (DRGs), but given the longer and more complex care needs in LTCHs, they get a higher tariff. To discourage admitting patients purely to get higher levels of reimbursement, the bulk of the payment only kicks in after a certain length of stay. Like I said – you can guess what happened.

This article shows 26% of patients are discharged in the three days after the length of stay threshold compared to just 7% in the three days prior. This pattern is most strongly observed in discharges to home, and is not present in patients who die. But this may still be just by chance that the threshold and these discharges coincide. Fortunately for the authors the thresholds differ between DRGs and even move around within a DRG over time in a way that appears unrelated to actual patient health. They therefore estimate a set of decision models for patient discharge to try to estimate the effect of different reimbursement policies.

Estimating misreporting in condom use and its determinants among sex workers: Evidence from the list randomisation method. Health Economics. Published November 2018.

Working on health and health care research, especially if you conduct surveys, means you often want to ask people about sensitive topics. These could include sex and sexuality, bodily function, mood, or other ailments. For example, I work a fair bit on sanitation, where frequently self-reported diarrhoea in under fives (reported by the mother that is) is the primary outcome. This could be poorly reported particularly if an intervention includes any kind of educational component that suggests it could be the mother’s fault for, say, not washing her hands, if the child gets diarrhoea. This article looks at condom use among female sex workers in Senegal, another potentially sensitive topic, since unprotected sex is seen as risky. To try and get at the true prevalence of condom use, the authors use a ‘list randomisation’ method. This randomises survey participants to two sets of questions: a set of non-sensitive statements, or the same set of statements with the sensitive question thrown in. All respondents have to do is report the number of the statements they agree with. This means it is generally not possible to distinguish the response to the sensitive question, but the difference in average number of statements reported between the two groups gives an unbiased estimator for the population proportion. Neat, huh? Ultimately the authors report an estimate of 80% of sex workers using condoms, which compares to the 97% who said they used a condom when asked directly.

 

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