Are user fees a barrier to health care in poor countries?

The 1987 Bamako declaration promoted user or consultation fees for health care as a means to raise revenue and improve the quality of services. However, user fees may pose a barrier to access, and hence the key Sustainable Development Goal of Universal Health Coverage (UHC), for the global poor who typically have a high elasticity of demand for health services. The evidence has been mixed though on the impact of adding or removing user fees. A Cochrane review found that utilisation of services typically declined significantly with the introduction of fees and that quality was often found to improve with fees, but they also questioned the reliability of these studies due to a “high risk of bias”. Indeed, the evidence can be conflicting as to the effect of user fees on health service utilisation. Consider the following two studies from two similar countries: Malawi and Zambia.

The first looks at the effect the introduction and removal user fees had on health centre outpatient attendances, new diagnoses of malaria, and HIV diagnoses in a rural district of Malawi (which I should declare I authored!) Of 13 centres in Neno district, four were operated by the Christian Health Association of Malawi, of which one has always charged user fees and three introduced them in July 2013. The other centres were operated by the Ministry for Health and an NGO, Partners In Health, and did not charge fees. In July 2015, one centre removed user fees. These changes in charging status created a neat natural experiment. A plot of outpatient attendances shows what happened:

Figure 3

Even without modelling it is clear what happened – attendances dropped with the introduction of user fees and increased when they were removed. Similar changes were seen in new malaria and HIV diagnoses Further analysis also suggested patients weren’t moving between centres to avoid fees.

The second study, published this week, looks at a 2006 policy to remove user fees for publicly-funded health care facilities in rural districts across Zambia. The policy was instigated by the Zambian president as a step towards UHC, but was implemented haphazardly with funding not being completely in place and districts choosing to distribute the funding they received in different ways. Using data from a repeated cross-sectional health survey, the corresponding plot of the effects of the policy is:

userfees

Evident from this and reinforced by their synthetic control analysis, the policy did little to change the proportion of people seeking health care. The key impact of the policy was to reduce out of pocket expenditure as it seems people switched from using private providers to public providers. So why do the results of these studies, with seemingly similar ‘treatments’ in similar poor rural populations, differ so much?

In an earlier study of the Zambian policy it was found that outpatient attendances recorded in routine data – the same data used in the Malawi study above – there were large increases in use of public facilities when user fees were removed. The new study adds evidence though that this increase was a result of people switching from private to public providers. In Neno district, Malawi there are no private providers – only those in the study. Nevertheless, private providers also charge, so health care use in the face of fees was markedly higher in Zambia than Neno, Malawi. Perhaps there are relevant differences then in the populations under study.

Zambia, even in 2006, was much wealthier than Malawi in 2013. GDP per capita in comparable dollars was $1,030 in 2006 Zambia and $333 in 2013 Malawi. And Neno district is among the poorest in Malawi. The Malawi study population may be significantly poorer then than that in Zambia, and so have yet more elastic demand. Then again, Zambia is one of the most unequal countries in the world, its wealth generated from a boom in the copper price and other commodities. Its Gini coefficient is 57.5 as compared to Malawi’s 43.9. Thus, one may expect rural Zambians to perhaps be comparable to those in Malawi despite GDP differences. Unfortunately, there aren’t further statistics in the paper to compare the samples – and indeed no information on the relative prices of the user fees. And further, the Zambia paper does look at the poorest 50% of people separately and finds little difference in the treatment effect although there does appear to be large levels of heterogeneity in the estimated treatment effects between districts.

Differences in conclusions may also results from differences in data. For example, the Zambia study looked at changes in the reported use of formal health care among people who had an illness recently, whereas the Malawian study looked at outpatient attendances and diagnoses. Perhaps a difference could arise here such as reporting biases in the survey data.

It is not clear why results in the Zambian study differ from those in the Malawian one and indeed many others. It certainly shows the difficulty we have understanding the effect even small charges can have on access to care even as the quality of the evidence improves.

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Sam Watson’s journal round up for 16th October 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.

Effect of forced displacement on health. Journal of the Royal Statistical Society: Series A [RePEcPublished October 2017

History, as they say, is doomed to repeat itself. Despite repeated cries of ‘never again’, war and conflict continue to harm and displace people around the world. The mass displacement of the Rohingya in Myanmar is leading to the formation of the world’s largest refugee camp in Bangladesh. Aside from the obvious harm from conflict itself, displacement is likely to have pernicious effects on health. Livelihoods and well-being is lost as well as access to basic amenities and facilities. The conflict in Croatia and wider Eastern Europe created a mass displacement of people, however, many went to relatively wealthy neighbouring countries across Europe. Thus, the health effects of displacement in this conflict should provide an understanding of the lower bound of what happens. This paper looks into this question using a health survey of Croatians from 2003. An empirical issue the authors spend a substantial amount of time addressing is that displacement status is likely to be endogenous: beyond choices about protecting their household and possessions, health and the ability to travel may play a large role in decisions to move. The mortality rate from conflict is used as an instrument for displacement, being a proxy for the intensity of war. However, conflict intensity is obviously likely to have a effect itself on health status. A method of relaxing the exclusion restriction is used, which tempers the estimates somewhat. Nevertheless, there is evidence that displacement impacts upon hypertension, self-assessed health, and emotional and physical dimensions of the SF-36. However, it seems to me that there may be another empirical issue not dealt with – the sample selection problem. While the number of casualties was low relative to the size of the population and numbers of displaced people, those who died obviously don’t feature in the sample. And those who died may have also been more likely or not to be displaced and be in worse health. Maybe only a bias of second order but a point it seems is left unconsidered.

Can variation in subgroups’ average treatment effects explain treatment effect heterogeneity? Evidence from a social experiment. Review of Economics and Statistics [RePEcPublished October 2017

A common approach to explore treatment effect heterogeneity is to estimate mean impacts by subgroups. In applied health economics studies I have most often seen this done by pooling data and adding interactions of the treatment with subgroups of interest to a regression model. For example, there is a large interest in differences in access to care across socioeconomic groups – in the UK we often use quintiles, or other division, of the Index of Multiple Deprivation, which is estimated at small area level, to look at this. However, this paper looks at the question of whether this approach to estimating heterogeneity is any good. Using data from a large jobs treatment program, they compare estimates of quantile treatment effects, which are considered to fully capture treatment effect heterogeneity, to results from various specifications of models that assume constant treatment effects within subgroups. If they found there was little difference in the two methods, I doubt the paper would have been published in such a good journal, so it’s no surprise that their conclusions are that the subgroup models perform poorly. Even allowing for more flexibility, such as by allowing effects to vary over time, and adding submodels for a point mass at zero, they still don’t do that well. Interestingly, subgroups defined according to different variables, e.g. education or pre-treatment earnings, fare differently – so comparisons across types of subgroups is important when the analyst is looking at heterogeneity. The takeaway message though is that constant effects subgroups models aren’t that good – more flexible semi or nonparametric methods may be preferred.

The hidden costs of terrorism: The effects on health at birth. Journal of Health Economics [PubMedPublished October 2017

We here at the blog have covered a long series of papers on the effects of in utero stressors on birth and later life health and economic outcomes. The so-called fetal-origins hypothesis posits that the nine months in the womb are some of the most important in predicting later life health outcomes. This may be one of the main mechanisms explaining intergenerational transmission of health. Some of these previous studies have covered reduced maternal nutrition, exposure to conditions of famine, or unemployment shocks in the household. This study examines the effect of the mother being pregnant in a province in Spain during which a terrorist attack by ETA occurred. At first glance, one might be forgiven for being sceptical at first, given (i) terrorist attacks were rare, (ii) the chances of actually being affected by an attack in a province if an attack occurred is low, so (iii) the chances are that the effect of feeling stressed on birth weight is small and likely to be swamped by a multitude of other factors (see all the literature we’ve covered on the topic!) All credit to the authors for doing a thorough job of trying to address all these concerns, but I’ll admit I remain sceptical. The effect sizes are very small indeed, as we suspected, and unfortunately there is not enough evidence to examine whether those women who had low birth weight live births were stressed or demonstrating adverse health behaviours.

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Ambulance and economics

I have recently been watching the BBC series AmbulanceIt is a fly-on-the-wall documentary following the West Midlands Ambulance Service interspersed with candid interviews with ambulance staff, much in the same vein as other health care documentaries like 24 Hours in A&EAs much as anything it provides a (stylised) look at the conditions on the ground for staff and illustrates how health care institutions are as much social institutions as essential services. In a recent episode, the cost of a hoax call was noted as some thousands of pounds. Indeed, the media and health services often talk about the cost of hoax calls in this way:

Warning for parents as one hoax call costs public £2,465 and diverts ambulance from real emergency call.

Frequent 999 callers cost NHS millions of pounds a year.

Nuisance caller cost the taxpayer £78,000 by making 408 calls to the ambulance service in two years.

But these are accounting costs, not the full economic cost. The first headline almost captures this by suggesting the opportunity cost was attendance at a real emergency call. However, given the way that ambulance resources are deployed and triaged across calls, it is very difficult to say what the opportunity cost is: what would be the marginal benefit of having an additional ambulance crew for the duration of a hoax call? What is the shadow price of an ambulance unit?

Few studies have looked at this question. The widely discussed study by Claxton et al. in the UK, looked at shadow prices of health care across different types of care, but noted that:

Expenditure on, for example, community care, A&E, ambulance services, and outpatients can be difficult to attribute to a particular [program budget category].

One review identified a small number of studies examining the cost-benefit and cost-effectiveness of emergency response services. Estimates of the marginal cost per life saved ranged from approximately $5,000 to $50,000. However, this doesn’t really tell us the impact of an additional crew, nor were many of these studies comparable in terms of the types of services they looked at, and these were all US-based.

There does exist the appropriately titled paper Ambulance EconomicsThis paper approaches the question we’re interested in, in the following way:

The centrepiece of our analysis is what we call the Ambulance Response Curve (ARC). This shows the relationship between the response time for an individual call (r) and the number of ambulances available and not in use (n) at the time the call was made. For example, let us suppose that 35 ambulances are on duty and 10 of them are being used. Then n has the value of 25 when the next call is taken. Ceteris paribus, as increases, we expect that r will fall.

On this basis, one can look at how an additional ambulance affects response times, on average. One might then be able to extrapolate the health effects of that delay. This paper suggests that an additional ambulance would reduce response times by around nine seconds on average for the service they looked at – not actually very much. However, the data are 20 years old, and significant changes to demand and supply over that period are likely to have a large effect on the ARC. Nevertheless, changes in response time of the order of minutes are required in order to have a clinically significant impact on survival, which are unlikely to occur with one additional ambulance.

Taken altogether, the opportunity cost of a hoax call is not likely to be large. This is not to downplay the stupidity of such calls, but it is perhaps reassuring that lives are not likely to be in the balance and is a testament to the ability of the service to appropriately deploy their limited resources.

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