Tag Archives: Director & Co-Directors’ Blog

50 Year Anniversary of the First Human Heart Transplant: Lessons for Today

On 3 December we commemorated the 50 year anniversary of the world’s first heart transplant. The operation took place in the early hours of a Saturday morning at the Groote Schuur hospital in Cape Town, South Africa. Christiaan Barnard sutured Denise Darvall’s donated heart into the chest of the recipient, Louis Washkansky. Barnard restarted the new heart with an electric shock and then tried to wean the recipient off the heart and lung machine. But the new heart could not take the strain and Washkansky had to go back on the machine. The second attempt also failed, but when the heart and lung machine was turned off for the third time the recipient’s blood pressure started to climb. It kept on climbing, and soon Denise Darvall’s small heart had taken over the perfusion of Louis Washkansky’s large frame. Later that morning the world woke to the news of the world’s first heart transplant. Looking back over fifty years what should we make of Barnard’s achievement?

The transplant in an historical perspective

The two decades preceding the heart transplant have sometimes been referred to as the golden age of medical discovery.[1] The transplant can be ‘fitted’ retrospectively as the culmination of this golden age just as Neil Armstrong’s moon walk, two years later, can be seen as the crowning achievement of the space race. They belong to a number of technical achievements, including the first “test tube” baby and the first man in space, which are emblematic of human progress. They generate great public interest and media attention, but differ from more fundamental intellectual discoveries, such as the double helix in DNA or Higgs boson, that are rewarded with Nobel prizes.

The heart transplant in the ‘heroic’ medical age

In his book ‘One Life’ Barnard provides an interesting cameo of the power and autonomy of the medical profession in his time.[2] He recalls writing up the routine operation note that must follow any surgical procedure. The anaesthetist, ‘Oz’, suggested that Dr Jacobus Burger, the hospital superintendent, should be informed. Barnard asked whether he should wake him so early in the morning, but Oz replied that the night’s events warranted such an intrusion. At first the befuddled Dr Burger, aware if work in the animal lab, thought that he was being informed about another heart transplant in dogs. However, even when he learned that the transplant involved a human heart, he cryptically thanked the surgeon and replaced the receiver. Nowadays, the idea of carrying out a procedure of such novelty, cost and risk without formal sanction would be unfathomable. The vignette from the doctor’s tearoom vividly illustrates how the relationship between the medical profession and the broader society has changed over one generation. Rene Amalberti argues [3] that many professions progressed through a heroic age in the twentieth century before gradually becoming more formalised and regulated – aviation followed a similar trajectory following Charles Lindbergh’s dramatic flight across the Atlantic in 1927.

Gradually changing ethical norms

The ethics of heart transplants relate mainly to organ donation. In ‘One Life’ Barnard describes the tense atmosphere in the operating room as the team waited for the donor heart to stop after turning off Darvall’s ventilator. In fact, they did not wait, and Barnard’s brother Marius has stated he persuaded Christiaan to stop the donor heart by injecting a concentrated dose of potassium in order to give Washkansky the best chance of survival. Today two different doctors need to independently carry out tests to confirm the donor is brain stem dead before the heart can be removed, as opposed to waiting for death by the whole-body standard, i.e. when there is brain death and the heart has stopped beating.

Public views of heart transplants, then and now

Following the operation the exhausted Barnard went home for a sleep. In the afternoon he returned to the hospital where he was surprised to find his route obstructed by a large crowd of reporters. He had unleashed a tide of publicity and acclaim that resonated for many decades, but dissenting voices were also heard. Some, notably Malcolm Muggeridge, the editor of Punch magazine, attacked the operation on the basis of a near mystical reverence for the human heart and to this Barnard had a succinct response: “it’s merely a pump.” Others worried about the allocation of scarce resources to such a high-tech solution when people were dying from malnutrition and malaria. Defence of the procedure came, albeit years later, from the economics profession when it was shown that the operation has a highly favourable cost-to-benefit ratio (at least in a high-income country).[4] The procedure not only extends life by many years on average, but greatly improves the quality of that life. In fact, patients feel much better from the moment they regain consciousness after the operation despite pain from the sternotomy. The operation is now uncontroversial and is performed routinely in high-income countries. It was long predicted that a mechanical pump would supplant the need for transplantation. Mechanical hearts have improved,[5] but they are largely seen as a bridge to transplantation, rather than a better alternative.

If Christiaan Barnard had not performed his operation, heart transplants would have developed anyway (the second transplant was carried out independently by Adrian Kantrowitz in the USA on 6 December). I was a school boy with hopes of getting into medical school when Washkansky received his new heart. I was among the many millions who were swept up in the wonder of the event and it still stirs my imagination half a century later. And my family knows that I wish to donate my own heart if the circumstances arise.

— Richard Lilford, CLAHRC WM Director

References:

  1. Lilford RJ. Future Trends in NHS. NIHR CLAHRC West Midlands. 25 November 2016.
  2. Barnard C & Pepper CB. One Life. Toronto, Canada: Macmillan; 1969.
  3. Amalberti R. The paradoxes of almost totally safe transportation systems. Saf Sci. 2001; 37(2-3): 109-26.
  4. O’Brien BJ, Buxton MJ, Ferguson BA. Measuring the effectiveness of heart transplant programmes: Quality of life data and their relationship to survival analysis. J Chron Dis. 1987; 40(s1): s137-53.
  5. Girling AJ, Freeman G, Gordon JP, Poole-Wilson P, Scott DA, Lilford RJ. Modeling payback from research into the efficacy of left-ventricular assist devices as destination therapyInt J Technol Assess Health Care. 2007; 23(2): 269-77.
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Is Research Productivity on the Decline Internationally?

I have written previously on the so-called ‘golden age of medical research,’ [1] which coincides roughly with the first two decades of my life – 1950-1970. The premise of a golden age entails the conclusion that it is followed by a less spectacular age where marginal returns are lower per unit of input – say per researcher. So, where does the truth lie – is research becoming ever more efficient, or is the productivity of research declining? This subject has been carefully examined by a number of scholars, most recently by Bloom and others.[2] First they looked at aggregate supply of researchers and economic output across the US economy, and they found a relationship that looks like this:

091 DCB Figure 1

So, productivity per researcher appears to decline with time and does so quite rapidly – the graph uses log scales. The drop in unit productivity has been fully compensated by growth in the number of researchers.

Given the obvious problems of studying this phenomenon at the aggregate level, the researchers turn to individual topics, such as number of transistors packed onto a single chip. It turns out that keeping Moore’s law going takes a rapidly increasing number of researchers. However, diminishing returns are not just observed in electronics, the authors found the same phenomenon in agriculture and medicine. Research productivity in the pharmaceutical industry is one-tenth of what it was in 1970, and mortality gains have peaked in cancer and in heart disease. To some extent one can see this effect in the number of authors of medical papers, such as those in genetic epidemiology – they often run literally into hundreds. It would appear that ideas really are getting harder to find and/or when found they portend smaller gains.

I have previously made the obvious point that improved care reduces the headroom for future improvements.[3] Of course, economic growth and further improvement in health still turn on new knowledge and technology without which the supply-side of the economy must stagnate. The phenomenal growth of some emerging economies has been possible because of the non-rivalrous nature of previous discoveries made elsewhere. But we need to continue to advance for all that advances are hard to make. One of these advances concerns making optimal use of existing knowledge, and that is where CLAHRCs come into their own – we trade in knowledge about knowledge.

— Richard Lilford, CLAHRC WM Director

References:

  1. Lilford RJ. Future Trends in NHS. NIHR CLAHRC West Midlands. 25 November 2016.
  2. Bloom N, Jones CI, Van Reenen J, Webb M. Are Ideas Getting Harder to Find? Centre for Economic Performance Discussion Paper No. 1496. 2017.
  3. Lilford RJ. Patient Involvement in Patient Safety: Null Result from a High Quality Study. NIHR CLAHRC West Midlands. 18 August 2017.

Machine Learning and the Demise of the Standard Clinical Trial!

An increasing proportion of evaluations are based on database studies. There are many good reasons for this. First, there simply is not enough capacity to do randomised comparisons of all possible treatment variables.[1] Second, some treatment variables, such as ovarian removal during hysterectomy, are directed by patient choice rather than experimental imperative.[2] Third, certain outcomes, especially those contingent on diagnostic tests,[3] are simply too rare to evaluate by randomised trial methodology. In such cases, it is appropriate to turn to database studies. And when conducting database studies it is becoming increasingly common to use machine learning rather than standard statistical methods, such as logistic regression. This article is concerned with strengths and limitations of machine learning when databases are used to look for evidence of effectiveness.

When conducting database studies, it is right and proper to adjust for confounders and look for interaction effects. However, there is always a risk that unknown or unmeasured confounders will result in residual selection bias. Note that two types of selection are in play:

  1. Selection into the study.
  2. Once in the study, selection into one arm of the study or another.

Here we argue that while machine learning has advantages over RCTs with respect to the former type of bias, it cannot (completely) solve the problem of selection to one type of treatment vs. another.

Selection into a Study and Induction Across Place and Time (External Validity)
A machine learning system based on accumulating data across a health system has advantages with respect to the representativeness of the sample and generalisations across time and space.

First, there are no exclusions by potential participant or clinician choice that can make the sample non-representative of the population as a whole. It is true that the selection is limited to people who have reached the point where their data become available (it cannot include people who did not seek care, for example), but this caveat aside, the problem of selection into the study is strongly mitigated. (There is also the problem of ‘survivor bias’, where people are ‘missing’ from the control group because they have died, become ineligible or withdrawn from care. We shall return to this issue.)
Second, the machine can track (any) change in treatment effect over time, thereby providing further information to aid induction. For example, as a higher proportion of patients/ clinicians adopt a new treatment, so intervention effect can be examined. Of course, the problem is not totally solved, because the possibility of different effects in other health systems (not included in the database) still exists.

Selection Once in a Study (Internal Validity)
However, the machine cannot do much about selection to intervention vs. control conditions (beyond, perhaps, enabling more confounding variables to be taken into account). This is because it cannot get around the cause-effect problem that randomisation neatly solves by ensuring that unknown variables are distributed at random (leaving only lack of precision to worry about). Thus, machine learning might create the impression that a new intervention is beneficial when it is not. If the new intervention has nasty side-effects or high costs, then many patients could end up getting treatment that does more harm than good, or which fails to maximise value for money. Stability of results across strata does not vitiate the concern.

It could be argued, however, that selection effects are likely to attenuate as the intervention is rolled out over an increasing proportion of the population. Let us try a thought experiment. Consider the finding that accident victims who receive a transfusion have worse outcomes than those who do not, even after risk-adjustment. Is this because transfusion is harmful, or because clinicians can spot those who need transfusion, net of variables captured in statistical models? Let us now suppose that, in response to the findings, clinicians subsequently reduce use of transfusion. It is then possible that changes in the control rate and in the treatment effect can provide evidence for or against cause and effect explanations. The problem here is that bias may change as the proportions receiving one treatment or the other changes. There are thus two possible explanations for any set of results – a change in bias or a change in effectiveness, as a wider range of patients/ clinicians receive the experimental intervention. It is difficult to come up with a convincing way to resolve the cause and effect problem. I must leave it to someone cleverer than myself to devise a theorem that might shed at least some light on the plausibility of the competing explanations – bias vs. cause and effect. But I am pessimistic for this general reason. As a treatment is rolled out (because it seems effective) or withdrawn (because it seems ineffective or harmful), so the beneficial or harmful effect (even in relative risk ratio terms) is likely to attenuate. But the bias is also likely to attenuate because less selection is taking place. Thus the two competing explanations may be confounded.

There is also the question of whether database studies can mitigate ‘survivor bias’. When the process (of machine learning) starts, then survivor bias may exist. But, by tracking estimated treatment effect over time, the machine can recognise all subsequent ‘eligible’ cases as they arise. This means that the problem of survivor bias should be progressively mitigated over time?

So what do I recommend? Three suggestions:

  1. Use machine learning to provide a clue to things that you might not have suspected or thought of as high priority for a trial.
  2. Nest RCTs within database studies, so that cause and effect can be established at least under specified circumstances, and then compare the results with what you would have concluded by machine learning alone.
  3. Use machine learning on an open-ended basis with no fixed stopping point or stopping rule, and make data available regularly to mitigate the risk of over-interpreting a random high. This approach is very different to the standard ‘trial’ with a fixed starting and end data, data-monitoring committees,[4] ‘data-lock’, and all manner of highly standardised procedures. Likewise, it is different to resource heavy statistical analysis, which must be done sparingly. Perhaps that is the real point – machine learning is inexpensive (has low marginal costs) once an ongoing database has been established, and so we can take a ‘working approach’, rather than a ‘fixed time point’ approach to analysis.

— Richard Lilford, CLAHRC WM Director

References:

    1. Lilford RJ. The End of the Hegemony of Randomised Trials. 30 Nov 2012. [Online].
    2. Mytton J, Evison F, Chilton PJ, Lilford RJ. Removal of all ovarian tissue versus conserving ovarian tissue at time of hysterectomy in premenopausal patients with benign disease: study using routine data and data linkageBMJ. 2017; 356: j372.
    3. De Bono M, Fawdry RDS, Lilford RJ. Size of trials for evaluation of antenatal tests of fetal wellbeing in high risk pregnancy. J Perinat Med. 1990; 18(2): 77-87.
    4. Lilford RJ, Braunholtz D, Edwards S, Stevens A. Monitoring clinical trials—interim data should be publicly available. BMJ. 2001; 323: 441

 

Context is Everything in Service Delivery Research

I commonly come across colleagues who say that context is all in service delivery research. They argue that summative quantitative studies are not informative because there is so much variation by context that the average is meaningless. I think that this is lazy thinking. If context was all, then there would be no point in studying anything by any means; any one instance would be precisely that – one instance. If the effects of an intervention were entirely context-specific then it would never be permissible to extrapolate from one situation to another, irrespective of the types of observations made. But nobody thinks that.

A softer anti-quantitative view accepts the idea of generalising across contexts, but holds that such generalisations / extrapolations can be built up solely from studies of underlying mechanisms, and that in-depth qualitative studies can tell us all we need to know about those mechanisms. Proponents of this view hold that quantitative epidemiological studies are, at best, extremely limited in what they can offer. It is true that some things cannot easily be studied in a quantitative comparative way – an historian interested in the cause of the First World War cannot easily compare the candidate explanatory variables over lots of instances. In such a case, exploration of various individual factors that may have combined to unleash the catastrophe may be all that is available. But accepting this necessity is not tantamount to eschewing quantitative comparisons when they are possible. It is unsatisfying to study just the mechanisms by which improved nurse ratios may reduce falls or pressure ulcers without measuring whether the incidence of these outcomes is, in fact, correlated with nurse numbers.

Of course, concluding that quantification is important is not tantamount to concluding that quantification alone is adequate. It never is and cannot be, as the famous statistician, Sir Austin Bradford Hill, implied in his famous speech.[1] Putative causal explanations are generally strengthened when theory generated from one study yields an hypothesis that is supported by another study (Hegel’s thesis, antithesis, synthesis idea). Alternatively, or in addition, situations arise when evidence for a theory, and for hypotheses that are contingent on that theory, may arise within the framework of a single study. This can happen when observations are made across a causal chain. For example, a single study may follow up heavy, light and non-drinkers and examine the size of the memory centre in the brain (by MRI) and their memory (through a cognitive test).[2] The theory that alcohol affects memory is supported by the finding that memory declines faster in drinkers than teetotallers, and yet further support comes from alcohol’s effect on the size of the memory centre (the hippocampus). Similarly, a single study may show that improving the nurse to patient ratio results in a lower probability of unexpected deaths and more diligent monitoring of patients’ vital signs. Here the primary hypothesis that the explanatory variable (nurse/patient ratio) is correlated with the outcome variable (unexpected hospital death) is reinforced by also finding a correlation between the intervening / mediatory variable (diligence in monitoring vital signs) and the outcome variable (hospital deaths) (see Figure 1). In a previous News Blog we have extolled the virtues of Bayesian networking in quantifying these contingent relationships.[3]

088 DCB - Context Fig 1

Figure 1: Causal chain linking explanatory variable (intervention) and outcome

Observations relating to various primary and higher order hypotheses may be quantitative or qualitative. Qualitative observations on their own are seldom sufficient to test a theory and make reliable predictions. But measurement without a search for mechanisms – without representation / theory building – is effete. The practical value of science depends on ‘induction’ – making predictions over time and space. Such predictions across contexts require judgement, and such judgement cannot gain purchase without an understanding of how an intervention might work. Putting these thoughts together (the thesis, antithesis, synthesis idea and the need for induction), we end up with a ‘realist’ epistemology – the idea here is to make careful observations, interpret them according to the scientific canon, and then represent the theory – the underlying causal mechanisms. In such a framework, qualitative observations complement quantitative observations and vice-versa.

It is because results are sensitive to context that mechanistic / theoretical understanding is necessary. Context refers to things that vary from place to place and that might influence the (relative or absolute) effects of an intervention. It is also plausible to argue that context is more influential with respect to some types of intervention than others. Arguably, context is (even) more important in service delivery research than in clinical research. In that case, one might say that understanding mechanisms is even more important in service delivery research than in clinical research. At the (absolute) limit, if B always follows A, then sound predictions may be made in the absence of an understanding of mechanisms – the Sun was known to always come up in the East, even before rotation of the Earth was discovered. But scientific understanding requires more than just following the numbers. A chicken may be too quick to predict that a meal will follow egg-laying just because that has happened on 364 consecutive days, while failing to appreciate the underlying socioeconomic mechanisms that might land her on a dinner plate on the 365th day, in Bertrand Russell’s evocative example.[4]

Moving on from a purely epistemological argument, there is plenty of empirical data to show that many quantitative findings are replicated across a sufficient range of contexts to provide a useful guide to action. Here are some examples. The effect of ‘user fees’ and co-payments on consumption of health care are quite predictable – demand is inelastic on price, meaning that a relatively small increase in price, relative to average incomes, suppresses demand. Moreover, this applies irrespective of medical need,[5] and across low- and high-income countries.[6] Audit and feedback as a mechanism to improve the effectiveness of care has consistently positive, but small (about 8% change in relative risk) effects.[7] Self-care for diabetes is effective across many contexts.[8] Placing managers under risk of sanction has a high risk of inducing perverse behaviour when managers do not believe they can influence the outcome.[9] It is sometimes claimed that behavioural / organisational sciences are qualitatively distinct from natural sciences because they involve humans, and humans have volition. Quite apart from the fact that we are not the only animals with volition (we share this feature with other primates and cetaceans), the existence of self-determination does not mean that interventions will not have typical / average effects across groups or sub-groups of people.

The diabetes example, cited above, is particularly instructive because it makes the point that the role of context is amenable to quantitative evaluation – context may have no effect, it may modify an effect (but not vitiate it), it may obliterate an effect, or even reverse the direction of an effect. Tricco’s iconic diabetes study [8] combined over 120 RCTs of service interventions to improve diabetes care (there are now many more studies and the review is being updated). The study shows not just how the effect of interventions vary by intervention type, but also how the intervention effect itself varies by context. It is thus untenable to claim, as some do, that ‘what works for whom, under what circumstances’ is discernible only by qualitative methods.[10] The development economist, Abhijit Banerjee, goes further, arguing that the main purpose of RCTs is to generate unbiased point estimates of effectiveness for use in observational studies of the moderating effect of context on intervention effects.[11]

We have defined context as all the things that might vary from place to place and that might affect intervention effects. Some people conflate context with how an intervention is taken up / modified in a system. This is a conceptual error – how the intervention is applied in a system is an effect of the intervention and like other effects, it may be influenced by context. Likewise, everything that happens ‘downstream’ of an intervention as a result of the intervention is a potential effect, and again, this effect may be affected by context.[12] Context includes upstream variables (see Figure 2) and any downstream variable at baseline. All that having been said, it is not always easy to distinguish when a change in a downstream variable is caused by the intervention, or whether it is a change in a variable that would have happened anyway (i.e. a temporal effect). Note, that a variable such as the nurse-patient ratio may be an intervention in one study (e.g. a study of nurse-patient ratios) and a context variable in another (e.g. a study of an educational intervention to reduce falls in hospital). Context is defined by its role in the inferential cause / effect framework, not by the kind of variable it is.

088 DCB - Context Fig 2

Figure 2: How to conceptualise the intervention, the effects downstream, and the context.

— Richard Lilford, CLAHRC WM Director

References:

  1. Hill AB. The environment and disease: Association or causation? Proc R Soc Med. 1965; 58(5): 295-300.
  2. Topiwala A, Allan C, Valkanova V, et al. Moderate alcohol consumption as risk factor for adverse brain outcomes and cognitive decline: longitudinal cohort studyBMJ. 2017; 357:j2353.
  3. Lilford RJ. Statistics is Far Too Important to Leave to Statisticians. NIHR CLAHRC West Midlands News Blog. 27 June 2014.
  4. Russell B. Chapter VI. On Induction. In: Problems of Philosophy. New York, NY: Henry Holt and Company, 1912.
  5. Watson SI, Wroe EB, Dunbar EL, Mukherjee J, Squire SB, Nazimera L, Dullie L, Lilford RJ. The impact of user fees on health services utilization and infectious disease diagnoses in Neno District, Malawi: a longitudinal, quasi-experimental study. BMC Health Serv Res. 2016; 16(1): 595.
  6. Lagarde M & Palmer N. The impact of user fees on health service utilization in low- and middle-income countries: how strong is the evidence? Bull World Health Organ. 2008; 86(11): 839-48.
  7. Effective Practice and Organisation of Care (EPOC). EPOC Resources for review authors. Oslo: Norwegian Knowledge Centre for the Health Services; 2015.
  8. Tricco AC, Ivers NM, Grimshaw JM, Moher D, Turner L, Galipeau J, et al. Effectiveness of quality improvement strategies on the management of diabetes: a systematic review and meta-analysis. Lancet. 2012; 379: 2252–61.
  9. Lilford RJ. Discontinuities in Data – a Neat Statistical Method to Detect Distorted Reporting in Response to Incentives. NIHR CLAHRC West Midlands News Blog. 1 September 2017.
  10. Pawson R & Tilley N. Realistic Evaluation. London: Sage. 1997.
  11. Banerjee AV & Duflo E. The Economic Lives of the Poor. J Econ Perspect. 2007; 21(1): 141-67.
  12. Lilford RJ, Chilton PJ, Hemming K, Girling AJ, Taylor CA, Barach P. Evaluating policy and service interventions: framework to guide selection and interpretation of study end points. BMJ. 2010; 341: c4413.

Towards a Unifying Theory for the Development of Health and Social Services as the Economy Develops in Countries

In a previous news blog I proposed grassroots solutions to the transportation of critically ill patients to hospital.[1] Other work has demonstrated the effectiveness of community action groups in many contexts, such as maternity care.[2] More recently I have read that the Kenyan government is proposing a combination of local authority and community action (Water Sector Trust Fund) to improve water and sewage in urban settlements.[3] The idea is for the local authority to provide the basic pipe infrastructure and then for local communities to establish linkages to bring water and sewage into homes. The government does not merely lay pipes, but also stimulates local involvement, including local subsidies and micro-enterprises. This epitomises collaboration between authorities and community groups.

In an extremely poor, post-conflict country, such as South Sudan, it is hard to find activities where the authorities and local people work together to improve health and wellbeing. On the other hand, in extremely rich countries like Norway and Switzerland, the government provides almost all that is required; all the citizen has to do is walk into the bathroom and turn on the tap.

The idea that is provoked by these many observations is that different solutions suit different countries at different points in their development. So much so obvious. Elaboration of the idea would go something like this. When a country is at the bottom end of the distribution for wealth, there is very little to be done other than put the basics of governance and law and order into place and try to reduce corruption. Once the country becomes more organised and slightly better off, a mixture of bottom-up and top-down solutions should be implemented. At this point, the tax base is simply too small for totally top-down, Norwegian style, solutions. In effect the bottom-up contribution makes good the tax deficit – it is a type of local and voluntary taxation. As the economy grows and as the middle class expands, the tax base increases and the government can take a larger role in funding and procuring (or providing) comprehensive services for its citizens.

This might seem anodyne written down as above. However, it is important to bear in mind that harm can be done by making the excellent the enemy of the good. Even before a substantial middle-class evolves in society, wealth is being generated. I recently visited a number of urban settlements (slums) in Nigeria, Pakistan and Kenya. All of these places were a hive of economic activity. This activity was mostly in the informal sector, generating small surpluses. Such wealth is invisible to the tax person, but it is there, and can be used. Using it requires organisation: “grit in the oyster”. The science base on how best to provide this ‘grit’ is gradually maturing. In order for it to do so, studies must be carried out across various types of community engagement and support. I expect this to be a maturing field of inquiry to which the global expansion of the CLAHRC message can contribute. Members of our CLAHRC WM team are engaging in such work through NIHR-funded programmes on health services and global surgery, and we hope to do so with regard to water and sanitation in the future.

— Richard Lilford, CLAHRC WM Director

References:

  1. Lilford RJ. Transport to Place of Care. NIHR CLAHRC West Midlands News Blog. 29 September 2017.
  2. Lilford RJ. Lay Community Health Workers. NIHR CLAHRC West Midlands News Blog. 10 April 2015.
  3. Water Sector Trust Fund, GIZ. Up-scaling Basic Sanitation for the Urban Poor (UBSUP) in Kenya. 2017.

Transport to Place of Care

Availability of emergency transport is taken for granted in high-income countries. The debate in such countries relates to such matters as the marginal advantages of helicopters over vehicle ambulances, and what to do when the emergency team arrives at the scene of an accident. But in low- or low-middle-income countries, the situation is very different – in Malawi, for example, there is no pretence that a comprehensive ambulance system exists. The subject of transport does not seem to get attention commensurate with its importance. Researchers love to study the easy stuff – role of particulates in lung disease; prevalence of diabetes in urban vs. rural areas; effectiveness of vaccines. But study selection should not depend solely on tractability – the scientific spotlight should also encompass topics that are more difficult to pin down, but which are critically important. Transport of critically ill patients falls into this category.[1]

Time is of the essence for many conditions. Maternity care is an archetypal example,[2] where delayed treatment in conditions such as placental abruption, eclampsia, ruptured uterus, and obstructed labour can be fatal for mother and child. The same applies to acute infections (most notably meningococcal meningitis) and trauma where time is critical (even if there is no abrupt cut-off following the so called ‘golden hour’).[3] The outcome for many surgical conditions is affected by delay during which, by way of example, an infected viscus may rupture, an incarcerated hernia may become gangrenous, or a patient with a ruptured tubal pregnancy might exsanguinate. However, in many low-income countries less than one patient in fifty has access to an ambulance service.[4] What is to be done?

The subject has been reviewed by Wilson and colleagues in a maternity care context.[5] Their review revealed a number of papers based on qualitative research. They find the theory that one might have anticipated – long delays, lack of infrastructure, and so on. They also make some less intuitive findings. People think that having an emergency vehicle at the ready could bring bad luck, and that it is shameful to expose oneself when experiencing vaginal bleeding.

Quite a lot of work has been done on the use of satellites to develop isochrones based on distances,[6] gradients, and road provision. But working out how long it should take to reach a hospital does not say much about how long it takes in the absence of a service for the transport of acutely sick patients.

We start from the premise that, for the time being at least, a fully-fledged ambulance service is beyond the affordability threshold for many low-income countries. However, we note that many people make it to hospital in an emergency even when no ambulance is available. This finding makes one think of ‘grass-roots’ solutions; finding ways to release the capacity inherent in communities in order to provide more rapid transfers. An interesting finding in Wilson’s paper is that few people, even very poor people, could not find the money for transfer to a place of care in a dire emergency. However, this does not square with work on acutely ill children in Malawi (Nicola Desmond, personal communication), nor work done by CLAHRC WM researchers showing the large effects that user fees have in supressing demand, especially for children, in the Neno province of Malawi.[7] In any event, a grass roots solution should be sought, pending the day when all injured or acutely ill people have access to an ambulance. Possible solutions include community risk-sharing schemes, incentives to promote local enterprises to transport sick people, and automatic credit transfer arrangements to reimburse those who provide emergency transport.

I am leading a work package for the NIHR Global Surgery Unit, based at the University of Birmingham, concerned with access to care. We will describe current practice across purposively sampled countries, work with local people to design a ‘solution’, conduct geographical and cost-benefit analyses, and then work with decision-makers to implement affordable and acceptable improvement programmes. These are likely to involve a system of local risk-sharing (community insurance), IT facilitated transfer of funds, promotion of local transport enterprises, community engagement, and awareness raising. We are very keen to collaborate with others who may be planning work on this important topic.

— Richard Lilford, CLAHRC WM Director

References:

  1. United Nations. The Millennium Development Goals Report 2007. New York: United Nations; 2007.
  2. Forster G, Simfukew V, Barber C. Use of intermediate mode of transport for patient transport: a literature review contrasted with the findings of Transaid Bicycle Ambulance project in Eastern Zambia. London: Transaid; 2009.
  3. Lord JM, Midwinter MJ, Chen Y-F, Belli A, Brohi K, Kovacs EJ, Koenderman L, Kubes P, Lilford RJ. The systemic immune response to trauma: an overview of pathophysiology and treatment. Lancet. 2014; 384(9952): 1455-65.
  4. Nyamandi V, Zibengwa E. Mobility and Health. 2007. In: Wilson A, Hillman S, Rosato M, Costello A, Hussein J, MacArthur C, Coomarasamy A. A systematic review and thematic synthesis of qualitative studies on maternal emergency transport in low- and middle-income countries. Int J Gynaecol Obstet. 2013; 122(3): 192-201.
  5. Wilson A, Hillman S, Rosato M, Skelton J, Costello A, Hussein J, MacArthur C, Coomarasamy A. A systematic review and thematic synthesis of qualitative studies on maternal emergency transport in low- and middle-income countries. Int J Gynaecol Obstet. 2013; 122(3): 192-201.
  6. Frew R, Higgs G, Harding J, Langford M. Investigating geospatial data usability from a health geography perspective using sensitivity analysis: The example of potential accessibility to primary healthcare. J Transp Health 2017 (In Press).
  7. Watson SI, Wroe EB, Dunbar EL, Mukherjee J, Squire SB, Nazimera L, Dullie L, Lilford RJ. The impact of user fees on health services utilization and infectious disease diagnoses in Neno District, Malawi: a longitudinal, quasi-experimental study. BMC Health Serv Res. 2016; 16(1): 595.

Stop Being Beastly to Malthus!

I never understand why people think that Malthus got it so badly wrong. His argument (the Malthusian trap) was that resources are finite and that, therefore, there must be some limit to the number of people that the world can feed.[1] While it certainly turned out that the world can feed many more people than he thought, this does not disprove the underlying theorem. At some point there must come a threshold, where food supply really fails to meet the demand. If we generalise from food to include water, then that point might not be as far away as complacent people think. Of course, we also have to take into account the environmental damage associated with feeding, transporting, and keeping a large number of people warm.

Malthus has become almost a figure of derision. While he may have been wrong about when, the jury is still out about whether. He was right about the generic point, that there is a limit to the carrying capacity of our planet. Food is central to this, because even if we do not run out of food, much environmental damage is caused in its production.

The world’s population will stabilise in about 50 years, although African populations will continue to expand for a while longer.[2] So we should mitigate the environmental effects of food production. I like to eat beef from time to time. However the production of beef is very energy intensive and the methane released by cattle contributes about 20% of the total global warming.[3] So I favour a tax on all beef, similar to that on fuel. Such a tax is more justifiable even, then a tax on sugar and tobacco. This is because consumption of sugar and tobacco does not have the strong externalities associated with fossil fuels and production of beef. There is no proper libertarian argument against taxation in circumstances where strong externalities apply.[4] Pigovian taxes are taxes designed to compensate for externalities and to reduce behaviour that harms others; they would seem entirely justified in this case. I am less of a fan of Pigovian taxes to deal with internalities – that is to stop people from harming themselves. But as it turns out, red meat is bad for our health, as discussed in a recent news blog.[5]

So let us give Malthus his due. He might have got the detail wrong, but his principle still stands. I vote for the rehabilitation of Malthus.

— Richard Lilford, CLAHRC WM Director

References:

  1. Malthus TR. An Essay on the Principle of Population. London, UK: J. Johnson, 1798.
  2. Lilford RJ. The Population of the World – Will Depend on What Happens in Africa. NIHR CLAHRC West Midlands News Blog. 9 January 2015.
  3. Steinfeld H, Gerber P, Wassenaar T, Castel V, Rosales M, de Hann C. Livestock’s Long Shadow: Environmental Issues and Options. Rome, Italy: Food and Agriculture Organization, 2006.
  4. Lilford RJ. An Issue of BMJ with Multiple Studies on Diet. NIHR CLAHRC West Midlands News Blog. 4 August 2017.
  5. Capewell S, Lilford R. Are nanny states healthier states? BMJ. 2016; 355: i6341.

A Secondary Sanitary Revolution? What About the First One?

Water and sanitation is being taken increasingly seriously in Low- and Middle-Income Countries (LMICs). This is a good thing because, despite improved treatment of diarrhoea and vaccination against rotavirus,[1] gastrointestinal diseases are one of the two biggest causes of death in children under the age of five.[2] Yet recent evaluations of water and sanitation interventions show patchy results [3] and are overall disappointing.[4] [5] Very few studies have been done in urban areas, but infant death rates in slums are unconscionably high.[6]

Why are water and sanitation interventions so disappointing in the LMICs of today when the Sanitary Revolution around the turn of the 19th century was so successful? Well it turns out that the Sanitary Revolution was a bit of a myth – Thomas McKeown, Professor of Social Medicine at the University of Birmingham, caused quite a stir by pointing this out in the 1970s.[7] The ‘historical epidemiology’ of this time period is intensely interesting. While sequential chlorination of water in North American cities in the early years of the 1900s was associated with corresponding dramatic drops in the incidence of typhoid fever,[8] establishment of water and sanitation in the Netherlands [9] and Estonia [10] produced no benefit whatsoever on gastrointestinal deaths. Only one-third of the reduction in gastrointestinal-related deaths observed in around the turn of the 18th century Germany could be attributed to water and sanitation improvements.[9]
So why do water and sanitation interventions produce such variable, and often disappointing, benefits? In rural India this can often be attributed to low use of facilities, but little or no health benefit has been observed, even when uptake has been high. A number of (non-exclusive) theories can be adduced:

1) The inadequate dose theory. This holds that the type of intervention deployed in LMICs has generally been inadequate. For example, pit latrines (classed as ‘improved sewerage’ by the UN) do not clean up the environment adequately.[11] Similarly, water pipes may be installed, but the water may be contaminated en route.[12] St Petersburg is a notorious example.

2) The tipping point theory. This theory is an elaboration of the above inadequate dose theory and postulates a non-linear relationship between the intensity, type of water and sanitation (facility), and coverage of interventions and health, with increasing and then decreasing returns to scale as shown in Figure 1. By this theory, many interventions (such as pit latrines) simply fail to reach the ‘tipping point’, especially in densely inhabited city areas.

085 DCB A Secondary Sanitary Revolution Fig 1

3) The deep contamination theory (Figure 2). By this theory contamination follows many routes and becomes embedded in local communities, with transmission routes that are frequently replenished, so that garbage dumps, flies, nappies, soil and the human gut all act as repositories of infection. Food may be contaminated along its supply chain, as well as in preparation. Floods sweep sewage out of drains and back into communities. Cleaning up such an environment moves the tipping point (shown in Figure 1) to the right (meaning it is harder to reach) and may also take time to effect – a point to which we return.

Microbiology

4) The multiple agent hypothesis. By this theory some germs are more easily eradicated than others. Typhoid is waterborne, but, unlike cholera, it cannot replicate in water. Ensuring uncontaminated water may be enough to eradicate this particular problem. However, hookworms are at the other end of the spectrum, since they can be carried asymptomatically and linger in soil. There is even some evidence that organisms gain virulence as they are passed rapidly from host to host.[13] So this theory predicts that some types of infection might decrease more rapidly than others in response to an intervention. Moreover, some real gains, with respect to some type of serious infection, might be obscured by little or no change in more common, but less serious infections.

5) The multiple causes theory. This theory relies on evidence that malnutrition and gastrointestinal infections are self-reinforcing. Certainly malnutrition is associated with an altered microbiome, which, in turn, reduces absorption of food, creating a vicious cycle.[14] The microbiome affects the immune system, which, in turn, affects resistance to infection.

6) The ‘double-handed’ hygiene hypothesis. Hygiene can compensate for dirty water and a contaminated environment, and some of the most consistently effective interventions in LMICs have been based on improved hygiene and near use decontamination.[4] [15] On the other hand, hygiene does not seem important in places where exposure to harmful pathogens is low and, in such circumstances, hygiene may be too fastidious, leading to allergic illnesses.

7) The insensitive outcome hypothesis. Measuring the health benefit of sanitation is not unproblematic – the standard question on diarrhoea enquires about loose stools over a three-day period, and the measurement error appears to be large.[16] An account of blood in stools, signifying dysentery (Shigella and amoeba) is more specific, but is much rarer, leading to imprecision (lack of statistical power). Anthropological measurements reflect long-term conditions, and many factors, including gastrointestinal infections and malnutrition (see above), and also age of weaning, birth weight, and mother’s weight (inter-generational effects). We are working on designing a better (equally sensitive, but more specific and less reactive) method to measure gastrointestinal health.[17]

There may well be an element of truth in all these hypotheses. If a fully functioning water and sewerage system was installed, lanes paved and drained, and garbage eliminated, then there would probably be an impressive and rapid improvement in gastrointestinal health, especially if malnutrition was also tackled. But the same water and sewerage system would probably have moderate and delayed benefits if not accompanied by the other measures mentioned. What nutrition and vaccination would achieve without water and sanitation is unknown, but as they are less expensive, the experiment should be tried in places where water and sanitation improvements are some time away. In-depth study of transmission routes will help explicate some of the other theories postulated and careful comparative studies will help identify the tipping point for the most cost-effective solutions. What is for sure is that science has a role to play in unravelling the process by which we may achieve a Second Sanitary Revolution.

— Richard Lilford, CLAHRC WM Director

References:

  1. GBD Diarrhoeal Diseases Collaborators. Estimates of global, regional, and national morbidity, mortality, and aetiologies of diarrhoeal diseases: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Infect Dis. 2017; 17: 909-48.
  2. Global Burden of Disease Pediatrics Collaboration. Global and National Burden of Diseases and Injuries Among Children and Adolescents Between 1990 and 2013. JAMA Pediatr. 2016; 170(3): 267-87.
  3. Lilford RJ, Oyebode O, Satterthwaite D, et al. Improving the health and welfare of people who live in slums. Lancet. 2017; 389: 559-70.
  4. Wolf J, Prüss-Ustün A, Cumming O, et al. Assessing the impact of drinking water and sanitation on diarrhoeal disease in low- and middle-income settings: systematic review and meta-regression. Trop Med Int Health. 2014; 19(8): 928-42.
  5. Fuller JA, Westphal JA, Kenney B, Eisenberg JNS. The joint effects of water and sanitation on diarrhoeal disease: a multicountry analysis of the Demographic and Health Surveys. Trop Med Int Health. 2015; 20(3): 284-92.
  6. Feikin DR, Olack B, Bigogo GM, et al. The burden of common infectious disease syndromes at the clinic and household level from population-based surveillance in rural and urban Kenya. PLoS One. 2011; 6: e16085.
  7. McKeown T, Record RG, Turner RD. An interpretation of the decline of mortality in England and Wales during the twentieth century. Popul Stud. 1975. 29(3): 391-422.
  8. Cutler D & Miller G. The Role of Public Health Improvements in Health Advances: The 20th Century United States. NBER Working Paper 10511. Cambridge, MA: National Bureau of Economic Research; 2004.
  9. Van Poppel F & van der Heijden C. The effects of water supply on infant and childhood mortality: a review of historical evidence. Health Trans Rev. 1997; 7(2): 113-48.
  10. Jaadla H & Puur A. The impact of water supply and sanitation on infant mortality: Individual-level evidence from Tartu, Estonia, 1897-1900. Popul Stud. 2016; 70(2): 163-79.
  11. Nakagiri A, Niwagaba CB, Nyenje PM, Kulabako RN, Tumuhairwe JB, Kansiime F. Are pit latrines in urban areas of sub-Saharan Africa performing? A review of usage, filling, insects and odour nuisances. BMC Public Health. 2016; 16: 120.
  12. Eschol J, Mahapatra P, Keshapagu S. Is fecal contamination of drinking water after collection associated with household water handling and hygiene practices? A study of urban slum households in Hyderabad, India. J Water Health. 2009; 7(1): 145-54.
  13. Ewald PW. Waterborne transmission and the evolution of virulence among gastrointestinal bacteria. Epidemiol Infect. 1991; 106: 83-119.
  14. Rook G, Bäckhed F, Levin BR, McFall-Ngai MJ, McLean AR. Evolution, human-microbe interactions, and life history plasticity. Lancet. 2017; 390: 521-30.
  15. Freeman MC, Garn JV, Sclar GD, Boisson S. The impact of sanitation on infectious disease and nutritional status: A systematic review and meta-analysis. Int J Hyg Environ Health. 2017; 220(6): 928-49.
  16. Schmidt WP, Arnold BF, Boisson S, et al. Epidemiological methods in diarrhoea studies – an update. Int J Epidemiol. 2011; 40(6): 1678-92.
  17. Lilford RJ. Protocol to Test Hypothesis That Streptococcal Infections and Their Sequelae Have Risen in Incidence Over the Last 14 Years in England. NIHR CLAHRC West Midlands News Blog. 13 January 2017.

Measuring the Quality of Health Care in Low-Income Settings

Measuring the quality of health care in High-Income Countries (HIC) is deceptively difficult, as shown by work carried out by many research groups, including CLAHRC WM.[1-5] However, a large amount of information is collected routinely by health care facilities in HICs. This data includes outcome data, such as Standardised Mortality Rates (SMRs), death rates from ’causes amenable to health care’, readmission rates, morbidity rates (such as pressure damage), and patient satisfaction, along with process data, such as waiting times, prescribing errors, and antibiotic use. There is controversy over many of these endpoints, and some are much better barometers of safety than others. While incident reporting systems provide a very poor basis for epidemiological studies (that is not their purpose), case-note review provides arguably the best and most widely used method for formal study of care quality – at least in hospitals.[3] [6] [7] Measuring safety in primary care is inhibited by the less comprehensive case-notes found in primary care settings as compared to hospital case-notes. Nevertheless, increasing amounts of process information is now available from general practices, particularly in countries (such as the UK) that collect this information routinely in electronic systems. It is possible, for example, to measure rates of statin prescriptions for people with high cardiovascular risk, and anticoagulants for people with ventricular fibrillation, as our CLAHRC has shown.[8] [9] HICs also conduct frequent audits of specific aspects of care – essentially by asking clinicians to fill in detailed pro formas for patients in various categories. For instance, National Audits in the UK have been carried out into all patients experiencing a myocardial infarction.[10] Direct observation of care has been used most often to understand barriers and facilitators to good practice, rather than to measure quality / safety in a quantitative way. However, routine data collection systems provide a measure of patient satisfaction with care – in the UK people who were admitted to hospital are surveyed on a regular basis [11] and general practices are required to arrange for anonymous patient feedback every year.[12] Mystery shoppers (simulated patients) have also been used from time to time, albeit not as a comparative epidemiological tool.[13]

This picture is very different in Low- and Middle-Income Countries (LMIC) and, again, it is yet more difficult to assess quality of out of hospital care than of hospital care.[14] Even in hospitals routine mortality data may not be available, let alone process data. An exception is the network of paediatric centres established in Kenya by Prof Michael English.[15] Occasionally large scale bespoke studies are carried out in LMICs – for example, a recent study in which CLAHRC WM participated, measured 30 day post-operative mortality rates in over 60 hospitals across low-, middle- and high-income countries.[16]

The quality and outcomes of care in community settings in LMICs is a woefully understudied area. We are attempting to correct this ‘dearth’ of information in a study in nine slums spread across four African and Asian countries. One of the largest obstacles to such a study is the very fragmented nature of health care provision in community settings in LMICs – a finding confirmed by a recent Lancet commission.[17] There are no routine data collection systems, and even deaths are not registered routinely. Where to start?

In this blog post I lay out a framework for measurement of quality from largely isolated providers, many of whom are unregulated, in a system where there is no routine system of data and no archive of case-notes. In such a constrained situation I can think of three (non-exclusive) types of study:

  1. Direct observation of the facilities where care is provided without actually observing care or its effects. Such observation is limited to some of the basic building blocks of a health care system – what services are present (e.g. number of pharmacies per 1,000 population) and availability (how often the pharmacy is open; how often a doctor / nurse / medical officer is available for consultation in a clinic). Such a ‘mapping’ exercise does not capture all care provided – e.g. it will miss hospital care and municipal / hospital-based outreach care, such as vaccination provided by Community Health Workers. It will also miss any IT based care using apps or online consultations.
  2. Direct observation of the care process by external observers. Researchers can observe care from close up, for example during consultations. Such observations can cover the humanity of care (which could be scored) and/or technical quality (which again could be scored against explicit standards and/or in a holistic (implicit) basis).[6] [7] An explicit standard would have to be based mainly on ‘if-then’ rules – e.g. if a patient complained of weight loss, excessive thirst, or recurrent boils, did the clinicians test their urine for sugar; if the patient complained of persistent productive cough and night sweats was a test for TB arranged? Implicit standards suffer from low reliability (high inter-observer variation).[18] Moreover, community providers in LMICs are arguably likely to be resistant to what they might perceive as an intrusive or even threatening form of observation. Those who permitted such scrutiny are unlikely to constitute a random sample. More vicarious observations – say of the length of consultations – would have some value, but might still be seen as intrusive. Provided some providers would permit direct observation, their results may represent an ‘upper bound’ on performance.
  3. Quality as assessed through the eyes of the patient / members of the public. Given the limitations of independent observation, the lack of anamnestic records of clinical encounters in the form of case-notes, absence of routine data, and likely limitations on access by independent direct observers, most information may need to be collected from patients themselves, or as we discuss, people masquerading as patients (simulated patients / mystery shoppers). The following types of data collection methods can be considered:
    1. Questions directed at members of the public regarding preventive services. So, households could be asked about vaccinations, surveillance (say for malnutrition), and their knowledge of screening services offered on a routine basis. This is likely to provide a fairly accurate measure of the quality of preventive services (provided the sampling strategy was carefully designed to yield a representative sample). This method could also provide information on advice and care provided through IT resources. This is a situation where some anamnestic data collection would be possible (with the permission of the respondent) since it would be possible to scroll back through the electronic ‘record’.
    2. Opinion surveys / debriefing following consultations. This method offers a viable alternative to observation of consultations and would be less expensive (though still not inexpensive). Information on the kindness / humanity of services could be easily obtained and quantified, along with ease of access to ambulatory and emergency care.[19] Measuring clinical quality would again rely on observations against a gold standard,[20] but given the large number of possible clinical scenarios standardising quality assessment would be tricky. However, a coarse-grained assessment would be possible and, given the low quality levels reported anecdotally, failure to achieve a high degree of standardisation might not vitiate collection of important information. Such a method might provide insights into the relative merits and demerits of traditional vs. modern health care, private vs. public, etc., provided that these differences were large.
    3. Simulated patients offering standardised clinical scenarios. This is arguably the optimal method of technical quality assessment in settings where case-notes are perfunctory or not available. Again, consultations could be scored for humanity of care and clinical/ technical competence, and again explicit and/or implicit standards could be used. However, we do not believe it would be ethical to use this method without obtaining assent from providers. There are some examples of successful use of the methods in LMICs.[21] [22] However, if my premise is accepted that providers must assent to use of simulated patients, then it is necessary to first establish trust between providers and academic teams, and this takes time. Again, there is a high probability that only the better providers will provide assent, in which case observations would likely represent ‘upper bounds’ on quality.

In conclusion, I think that the basic tools of quality assessment, in the current situation where direct observation and/or simulated patients are not acceptable, is a combination of:

  1. Direct observation of facilities that exist, along with ease of access to them, and
  2. Debriefing of people who have recently used the health facilities, or who might have received preventive services that are not based in these facilities.

We do not think that the above mentioned shortcomings of these methods is a reason to eschew assessment of service quality in community settings (such as slums) in LMICs – after all, one of the most powerful levers to improvement is quantitative evidence of current care quality.[23] [24] The perfect should not be the enemy of the good. Moreover, if the anecdotes I have heard regarding care quality (providers who hand out only three types of pill – red, yellow and blue; doctors and nurses who do not turn up for work; prescription of antibiotics for clearly non-infectious conditions) are even partly true, then these methods would be more than sufficient to document standards and compare them across types of provider and different settings.

— Richard Lilford, CLAHRC WM Director

References:

  1. Brown C, Hofer T, Johal A, Thomson R, Nicholl J, Franklin BD, Lilford RJ. An epistemology of patient safety research: a framework for study design and interpretation. Part 1. Conceptualising and developing interventions. Qual Saf Health Care. 2008; 17(3): 158-62.
  2. Brown C, Hofer T, Johal A, Thomson R, Nicholl J, Franklin BD, Lilford RJ. An epistemology of patient safety research: a framework for study design and interpretation. Part 2. Study design. Qual Saf Health Care. 2008; 17(3): 163-9.
  3. Brown C, Hofer T, Johal A, Thomson R, Nicholl J, Franklin BD, Lilford RJ. An epistemology of patient safety research: a framework for study design and interpretation. Part 3. End points and measurement. Qual Saf Health Care. 2008; 17(3): 170-7.
  4. Brown C, Hofer T, Johal A, Thomson R, Nicholl J, Franklin BD, Lilford RJ. An epistemology of patient safety research: a framework for study design and interpretation. Part 4. One size does not fit all. Qual Saf Health Care. 2008; 17(3): 178-81.
  5. Brown C, Lilford R. Evaluating service delivery interventions to enhance patient safety. BMJ. 2008; 337: a2764.
  6. Benning A, Ghaleb M, Suokas A, Dixon-Woods M, Dawson J, Barber N, et al. Large scale organisational intervention to improve patient safety in four UK hospitals: mixed method evaluation. BMJ. 2011; 342: d195.
  7. Benning A, Dixon-Woods M, Nwulu U, Ghaleb M, Dawson J, Barber N, et al. Multiple component patient safety intervention in English hospitals: controlled evaluation of second phase. BMJ. 2011; 342: d199.
  8. Finnikin S, Ryan R, Marshall T. Cohort study investigating the relationship between cholesterol, cardiovascular risk score and the prescribing of statins in UK primary care: study protocol. BMJ Open. 2016; 6(11): e013120.
  9. Adderley N, Ryan R, Marshall T. The role of contraindications in prescribing anticoagulants to patients with atrial fibrillation: a cross-sectional analysis of primary care data in the UK. Br J Gen Pract. 2017. [ePub].
  10. Herrett E, Smeeth L, Walker L, Weston C, on behalf of the MINAP Academic Group. The Myocardial Ischaemia National Audit Project (MINAP). Heart. 2010; 96: 1264-7.
  11. Care Quality Commission. Adult inpatient survey 2016. Newcastle-upon-Tyne, UK: Care Quality Commission, 2017.
  12. Ipsos MORI. GP Patient Survey. National Report. July 2017 Publication. London: NHS England, 2017.
  13. Grant C, Nicholas R, Moore L, Sailsbury C. An observational study comparing quality of care in walk-in centres with general practice and NHS Direct using standardised patients. BMJ. 2002; 324: 1556.
  14. Nolte E & McKee M. Measuring and evaluating performance. In: Smith RD & Hanson K (eds). Health Systems in Low- and Middle-Income Countries: An economic and policy perspective. Oxford: Oxford University Press; 2011.
  15. Tuti T, Bitok M, Malla L, Paton C, Muinga N, Gathara D, et al. Improving documentation of clinical care within a clinical information network: an essential initial step in efforts to understand and improve care in Kenyan hospitals. BMJ Global Health. 2016; 1(1): e000028.
  16. Global Surg Collaborative. Mortality of emergency abdominal surgery in high-, middle- and low-income countries. Br J Surg. 2016; 103(8): 971-88.
  17. McPake B, Hanson K. Managing the public-private mix to achieve universal health coverage. Lancet. 2016; 388: 622-30.
  18. Lilford R, Edwards A, Girling A, Hofer T, Di Tanna GL, Petty J, Nicholl J. Inter-rater reliability of case-note audit: a systematic review. J Health Serv Res Policy. 2007; 12(3): 173-80.
  19. Schoen C, Osborn R, Huynh PT, Doty M, Davis K, Zapert K, Peugh J. Primary Care and Health System Performance: Adults’ Experiences in Five Countries. Health Aff. 2004.
  20. Kruk ME & Freedman LP. Assessing health system performance in developing countries: A review of the literature. Health Policy. 2008; 85: 263-76.
  21. Smith F. Private local pharmacies in low- and middle-income countries: a review of interventions to enhance their role in public health. Trop Med Int Health. 2009; 14(3): 362-72.
  22. Satyanarayana S, Kwan A, Daniels B, Subbaramn R, McDowell A, Bergkvist S, et al. Use of standardised patients to assess antibiotic dispensing for tuberculosis by pharmacies in urban India: a cross-sectional study. Lancet Infect Dis. 2016; 16(11): 1261-8.
  23. Kudzma E C. Florence Nightingale and healthcare reform. Nurs Sci Q. 2006; 19(1): 61-4.
  24. Donabedian A. The end results of health care: Ernest Codman’s contribution to quality assessment and beyond. Milbank Q. 1989; 67(2): 233-56.

Patient and Public Involvement: Direct Involvement of Patient Representatives in Data Collection

It is widely accepted that the public and patient voice should be heard loud and clear in the selection of studies, in the design of those studies, and in the interpretation and dissemination of the findings. But what about involvement of patient and the public in the collection of data? Before science became professionalised, all scientists could have been considered members of the public. Robert Hooke, for example, could have called himself architect, philosopher, physicist, chemist, or just Hooke. Today, the public are involved in data collection in many scientific enterprises. For example, householders frequently contribute data on bird populations, and Prof Brian Cox involved the public in the detection of new planets in his highly acclaimed television series. In medicine, patients have been involved in collecting data; for example patients with primary biliary cirrhosis were the data collectors in a randomised trial.[1] However, the topic of public and patient involvement in data collection is deceptively complex. This is because there are numerous procedural safeguards governing access to users of the health service and that restrict disbursement of the funds that are used to pay for research.

Let us consider first the issue of access to patients. It is not permissible to collect research data without undergoing certain procedural checks; in the UK it is necessary to be ratified by the Disclosure and Barring Service (DBS) and to have necessary permissions from the institutional authorities. You simply cannot walk onto a hospital ward and start handing out questionnaires or collecting blood samples.

Then there is the question of training. Before collecting data from patients it is necessary to be trained in how to do so, covering both salient ethical and scientific principles. Such training is not without its costs, which takes us to the next issue.

Researchers are paid for their work and, irrespective of whether the funds are publically or privately provided, access to payment is governed by fiduciary and equality/diversity legislation and guidelines. Access to scarce resources is usually governed by some sort of competitive selection process.

None of the above should be taken as an argument against patients and the public taking part in data collection. It does, however, mean that this needs to be a carefully managed process. Of course things are very much simpler if access to patients is not required. For example, conducting a literature survey would require only that the person doing it was technically competent and in many cases members of the public would already have all, or some, of the necessary skills. I would be very happy to collaborate with a retired professor of physics (if anyone wants to volunteer!). But that is not the point. The point is that procedural safeguards must be applied, and this entails management structures that can manage the process.

Research may be carried out by accessing members of the public who are not patients, or at least who are not accessed through the health services. As far as I know there are no particular restrictions on doing so, and I guess that such contact is governed by the common law covering issues such as privacy, battery, assault, and so on. The situation becomes different, however, if access is achieved through a health service organisation, or conducted on behalf of an institution, such as a university. Then presumably any member of the public wishing to collect data from other members of the public would fall under the governance arrangements of the relevant institution. The institution would have to ensure not only that the study was ethical, but that the data-collectors had the necessary skills and that funds were disbursed in accordance with the law. Institutions already deploy ‘freelance’ researchers, so I presume that the necessary procedural arrangements are already in place.

This analysis was stimulated by a discussion in the PPI committee of CLAHRC West Midlands, and represents merely my personal reflections based on first principles. It does not represent my final, settled position, let alone that of the CLAHRC WM, or any other institution. Rather it is an invitation for further comment and analysis.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Browning J, Combes B, Mayo MJ. Long-term efficacy of sertraline as a treatment for cholestatic pruritus in patients with primary biliary cirrhosis. Am J Gastroenterol. 2003; 98: 2736-41.