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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Sam Watson’s journal round-up for 21st August 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.

Multidimensional performance assessment of public sector organisations using dominance criteria. Health Economics [RePEcPublished 18th August 2017

The empirical assessment of the performance or quality of public organisations such as health care providers is an interesting and oft-tackled problem. Despite the development of sophisticated methods in a large and growing literature, public bodies continue to use demonstrably inaccurate or misleading statistics such as the standardised mortality ratio (SMR). Apart from the issue that these statistics may not be very well correlated with underlying quality, organisations may improve on a given measure by sacrificing their performance on another outcome valued by different stakeholders. One example from a few years ago showed how hospital rankings based upon SMRs shifted significantly if one took into account readmission rates and their correlation with SMRs. This paper advances this thinking a step further by considering multiple outcomes potentially valued by stakeholders and using dominance criteria to compare hospitals. A hospital dominates another if it performs at least as well or better across all outcomes. Importantly, correlation between these measures is captured in a multilevel model. I am an advocate of this type of approach, that is, the use of multilevel models to combine information across multiple ‘dimensions’ of quality. Indeed, my only real criticism would be that it doesn’t go far enough! The multivariate normal model used in the paper assumes a linear relationship between outcomes in their conditional distributions. Similarly, an instrumental variable model is also used (using the now routine distance-to-health-facility instrumental variable) that also assumes a linear relationship between outcomes and ‘unobserved heterogeneity’. The complex behaviour of health care providers may well suggest these assumptions do not hold – for example, failing institutions may well show poor performance across the board, while other facilities are able to trade-off outcomes with one another. This would suggest a non-linear relationship. I’m also finding it hard to get my head around the IV model: in particular what the covariance matrix for the whole model is and if correlations are permitted in these models at multiple levels as well. Nevertheless, it’s an interesting take on the performance question, but my faith that decent methods like this will be used in practice continues to wane as organisations such as Dr Foster still dominate quality monitoring.

A simultaneous equation approach to estimating HIV prevalence with nonignorable missing responses. Journal of the American Statistical Association [RePEcPublished August 2017

Non-response is a problem encountered more often than not in survey based data collection. For many public health applications though, surveys are the primary way of determining the prevalence and distribution of disease, knowledge of which is required for effective public health policy. Methods such as multiple imputation can be used in the face of missing data, but this requires an assumption that the data are missing at random. For disease surveys this is unlikely to be true. For example, the stigma around HIV may make many people choose not to respond to an HIV survey, thus leading to a situation where data are missing not at random. This paper tackles the question of estimating HIV prevalence in the face of informative non-response. Most economists are familiar with the Heckman selection model, which is a way of correcting for sample selection bias. The Heckman model is typically estimated or viewed as a control function approach in which the residuals from a selection model are used in a model for the outcome of interest to control for unobserved heterogeneity. An alternative way of representing this model is as copula between a survey response variable and the response variable itself. This representation is more flexible and permits a variety of models for both selection and outcomes. This paper includes spatial effects (given the nature of disease transmission) not only in the selection and outcomes models, but also in the model for the mixing parameter between the two marginal distributions, which allows the degree of informative non-response to differ by location and be correlated over space. The instrumental variable used is the identity of the interviewer since different interviewers are expected to be more or less successful at collecting data independent of the status of the individual being interviewed.

Clustered multistate models with observation level random effects, mover–stayer effects and dynamic covariates: modelling transition intensities and sojourn times in a study of psoriatic arthritis. Journal of the Royal Statistical Society: Series C [ArXiv] Published 25th July 2017

Modelling the progression of disease accurately is important for economic evaluation. A delicate balance between bias and variance should be sought: a model too simple will be wrong for most people, a model too complex will be too uncertain. A huge range of models therefore exists from ‘simple’ decision trees to ‘complex’ patient-level simulations. A popular choice are multistate models, such as Markov models, which provide a convenient framework for examining the evolution of stochastic processes and systems. A common feature of such models is the Markov property, which is that the probability of moving to a given state is independent of what has happened previously. This can be relaxed by adding covariates to model transition properties that capture event history or other salient features. This paper provides a neat example of extending this approach further in the case of arthritis. The development of arthritic damage in a hand joint can be described by a multistate model, but there are obviously multiple joints in one hand. What is more, the outcomes in any one joint are not likely to be independent of one another. This paper describes a multilevel model of transition probabilities for multiple correlated processes along with other extensions like dynamic covariates and different mover-stayer probabilities.

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Sam Watson’s journal round-up for 24th July 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.

Ten years after the financial crisis: the long reach of austerity and its global impacts on health. Social Science & Medicine [PubMedPublished 22nd June 2017

The subject of austerity and its impact on health has generated its own subgenre in the academic literature. We have covered a number of papers on these journal round-ups on this topic, which, given the nature of economic papers, are generally quantitative in nature. However, while quantitative studies are necessary for generation of knowledge of the social world, they are not sufficient. At aggregate levels, quantitative studies may often rely on a black box approach. We may reasonably conclude a policy caused a change in some population-level indicator on the basis of a causal inference type paper, but we often need other types of evidence to answer why or how this occurred. A realist philosophy of social science may see this as a process of triangulation; at the very least it’s a process of abduction to develop theory that best explains what we observe. In clinical research, Bradford-Hill’s famous criteria can be used as a heuristic for causal inference: a cause can be attributed to an effect if it demonstrates a number of criteria including dose-response and reproducibility. For social science, we can conceive of a similar set of criteria. Effects must follow causes, there has to be a plausible mechanism, and so forth. This article in Social Science & Medicine introduces a themed issue on austerity and its effects on health. The issue contains a number of papers examining experiences of people with respect to austerity and how these may translate into changes in health. One example is a study in a Mozambican hospital and how health outcomes change in response to continued restructuring programs due to budget shortfalls. Another study explores the narrative of austerity in Guyana and it has long been sold as necessary for future benefits which never actually materialise. It is not immediately clear how austerity is being defined here, but it is presumably something like ‘a fiscal contraction that causes a significant increase in aggregate unemployment‘. In any case, it makes for interesting reading and complements economics research on the topic. It is a refreshing change from the bizarre ravings we featured a couple of weeks ago!

Home-to-home time — measuring what matters to patients and payers. New England Journal of Medicine [PubMedPublished 6th July 2017

Length of hospital stay is often used as a metric to evaluate hospital performance: for a given severity of illness, a shorter length of hospital stay may suggest higher quality care. However, hospitals can of course game these metrics, and they are further complicated by survival bias. Hospitals are further incentivised to reduce length of stay. For example, the move from per diem reimbursement to per episode had the effect of dramatically reducing length of stay in hospitals. As a patient recovers, they may no longer need hospital based care, the care they require may be adequately provided in other institutional settings. Although, in the UK there has been a significant issue with many patients convalescing in hospital for extended periods as they wait for a place in residential care homes. Thus from the perspective of the whole health system, length of stay in hospital may no longer be the right metric to evaluate performance. This article makes this argument and provides some interesting statistics. For example, between 2004 and 2011 the average length of stay in hospital among Medicare beneficiaries in the US decreased from 6.3 to 5.7 days; post-acute care stays increased from 4.8 to 6.0 days. Thus, the total time in care actually increased from 11.1 to 11.7 days over this period. In the post-acute care setting, Medicare still reimburses providers on a per diem basis, so total payments adjusted for inflation also increased. This article makes the argument that we need to structure incentives and reimbursement schemes across the whole care system if we want to ensure efficiency and equity.

The population health benefits of a healthy lifestyle: life expectancy increased and onset of disability delayed. Health Affairs [PubMedPublished July 2017

Obesity and tobacco smoking increase the risk of ill health and in so doing reduce life expectancy. The same goes for alcohol, although the relationship between alcohol consumption and risk of illness is less well understood. One goal of public health policy is to mitigate these risks. One successful way of communicating the risks of different behaviours is as changes to life expectancy, or conversely ‘effective age‘. From a different perspective, understanding how different risk factors affect life expectancy and disability-free life expectancy is important for cost-benefit analyses of different public health interventions. This study estimates life expectancy and disability-free life expectancy associated with smoking, obesity, and moderate alcohol consumption using the US-based Health and Retirement Study. However, I struggle to see how this study adds much; while it communicates its results well, it is, in essence, a series of univariate comparisons followed by a multivariate comparison. This has been done widely before, such as here and here. Nevertheless, the results reinforce those previous studies. For example, obesity reduced disability-free life expectancy by 3 years for men and 6 years for women.

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