Category Archives: CLAHRC WM

Sustainability and Transformation Plans in the English NHS

Sustainability and Transformation Plans (STPs) are the latest in a long line of approaches to strategic health care planning over a large population footprint. These latest iterations were based on a one million plus population, looked at a five year timescale, were led by local partners (often acute trusts, but sometimes, as in Birmingham and Solihull, by the Local Authority), and focused inevitably on financial pressures. The plans were published in December 2016 and now the challenge to the STP communities is further refinement of the plans and, of course, implementation.

The Health Service Journal (HSJ) reviewed the content of the STPs in November 2016 and highlighted three common and unsurprising areas of focus: further development of community based approaches to care (notably aligned to the New Models of Care discussed in the CLAHRC WM News Blog of 27 January; see also https://www.england.nhs.uk/ourwork/new-care-models/); reconfiguration of secondary and tertiary services; and sharing of back office and clinical support functions. More interestingly, the HSJ noted an absence of focus on social care, patient/ clinical/ wider stakeholder engagement and on prevention and wellbeing.

The King’s Fund has produced two reviews of how STPS have developed in November 2016 and February 2017. These have been based on interviews with the same sub set of leaders , as well as other analyses. Both have reached similar conclusions. Recommendations have included the need to: increase involvement of wider stakeholders; strengthen governance and accountability arrangements and leadership ( including full time teams ) to support implementation; support longer term transformation with money, e.g. new models of care, not just short term financial sustainability; stress-test assumptions and timescales to ensure they are credible and deliverable, then communicate with local populations about their implementation honestly; and finally, align national support behind their delivery, e.g. support, regulation, performance management and procurement guidance.

A specific recommendation relates to the need to ensure robust community alternatives are in place before hospital bed numbers are reduced. The service has received strong guidance about this latter point from NHS England in the last few weeks. Various other Thinktanks have also produced more or less hopeful commentaries on STPs, such as Reform, The Centre for Health and Public Interest and the IPPR; they all say they cannot be ignored.

Already, in March 2017, the context is shifting: yet again, ‘winter pressures’ have been high profile and require a NHS response; the scale of the social care crisis has become even more prominent; there is a national push to accelerate and support change in primary care provision.

Furthermore, the role of CCG is changing in response: some are merging to create bigger population bases which may or may not be the same as STP geography; some GP leaders are moving into the new primary care provider organisations; the majority of CCGs will be ‘doing their own’ primary care commissioning for the first time just as the pace of primary care change is increasing; some commissioning functions may shift to new care models such as accountable care arrangements. It is clear that for some geographies and services the STP approach could work, but more local and more national responses to specific services and in specific places will continue to be needed. All these issues will influence how the STPs play out in the local context.

— Denise McLellan

A Device for Failing Hearts

Back in 2005 I was approached by Sally Davies, then languishing as deputy director general of Research and Development, and asked to evaluate the utility of a trial of left ventricular assist devices (LVADs) for heart failure. We elicited a Bayesian prior from a chapter of surgeons from the American Society of Heart Surgeons. This prior was the basis for a value of information study,[1] which suggested that expensive LVAD technology might be a bridge too far for the hard-pressed NHS. Anyway, the world moves on and the New England Journal of Medicine has recently carried out a trial comparing two different LVADs, one more sophisticated (type 3) than the original version we studied (type 2).[2] The latest version had less problems with clotting up of the device, but survival free of a serious stroke at six months was similar, at over 80% in both groups – quite high considering how sick these patients were. The article has some extremely good diagrams explaining the devices. These devices are sometimes used to rest the heart, for example in a case of inflammation of the heart muscle. Most often they are used when the heart muscle packs up permanently, say as a result of heart attacks. In that case LVADs can be used to keep a person alive until a match can be found for a heart transplant, so called ‘bridge to transplant’, or as a permanent solution. However, I think the devices are themselves a bridge to a more subtle regenerative medicine approach based on stem cells.

— Richard Lilford, CLAHRC WM Director

References:

  1. 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 therapy. Int J Technol Assess Health Care. 2007; 23(2): 269-77.
  2. Mehrea MR, Naka Y, Uriel N, et al. A Fully Magnetically Levitated Circulatory Pump for Advanced Heart Failure. New Engl J Med. 2017; 376: 440-50.

More on Brain Health in Young Children and Effect on Life Course

Brain health in early childhood is a recurring theme of your News Blog. Peter Chilton referred me to an interesting article in Nature Human Behaviour published at the end of last year.[1] This study was based on a prospective study of children in the South Island of New Zealand. The investigators wanted to determine the prognosis for the 20% of the population with the worst brain health indicators at age three. These indicators include single parent family; low socioeconomic group; poor self-control; and low IQ. Outcome variables covered a range of important economically burdensome outcomes, such as obesity, cigarette smoking, and crime. These variables were harvested from various databases where health and crime statistics are recorded. A 20% ‘segment’ of this young population could be defined which predicted 80% of crime, and similar high rates on other outcomes. This 20:80 ratio, called the Pareto ratio, is often encountered in social science – for example, wealth distributes itself roughly in this proportion across many societies (about 20% of people control 80% of wealth). The authors say that their study shows plenty of ‘headroom’ for preventive interventions. That is to say, society could achieve massive gains if health and social outcomes among the highest risk segment could be improved to average levels. We have discussed interventions, such as early childhood education, before.[2-4] Many studies show statistically significant and economically worthwhile results for such interventions, but the gains come nowhere near the theoretical headroom defined here. Likely this is because brain health at age three is only partly the result of remediable factors.

— Richard Lilford, CLAHRC WM Director

References:

  1. Caspi A, Houts RM, Belsky DW, Harrington H, Hogan S, Ramrakha S, Poulton R, Moffitt TE. Childhood forecasting of a small segment of the population with large economic burden. Nature Hum Behav. 2016; 1: 0005.
  2. Lilford RJ. Pregnancy before age 16 – dropping quite rapidly from a peak in 1997. NIHR CLAHRC West Midlands News Blog. February 10, 2017.
  3. Lilford RJ. If you want to reduce partner violence or teenage pregnancy, then teach algebra and history? NIHR CLAHRC West Midlands News Blog. December 9, 2016.
  4. Lilford RJ. Evidence-based education (or how wrong the CLAHRC WM Director was). NIHR CLAHRC West Midlands News Blog. July 15, 2016.

Psychotropic and Anti-Addictive Medication After Release from Prison and Risk of Reoffending

Another great Scandinavian linkage study compares methods to reduce violent reoffending after release from Swedish jails, both across and within individual ex-prisoners.[1] The results confirm the results of RCTs in non-prisoner populations – psychotropic drugs reduce violent reoffending by about a third, and drugs to combat addiction by about 40% (using within person analysis to control for many sources of confounding, such as genetic predisposition, adverse upbringing, etc.). Similar results were obtained in the (potentially more confounded) between person analysis. Anti-depressant drugs had no apparent effect on reoffending in any analysis. Of course, this is an observational study and reverse causality, even within individuals is possible, but it is the best information we are likely to have for some time, and is relevant to attempts to reduce the duration of incarceration in many countries, including England. The fact that the results mirror experimental studies in at-risk people who had not been to prison adds verisimitude to the findings.[2]

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Chang Z, Lichtenstein P, Långström N, Larsson H, Fazel S. Association Between Prescription of Major Psychotropic Medications and Violent Reoffending After Prison Release. JAMA. 2016; 316(17): 1798-1807.
  2. Chen Y-F, Hemming K, Chilton PJ, Gupta KK, Altman DG, Lilford RJ. Scientific hypotheses can be tested by comparing the effects of one treatment over many diseases in a systematic review. J Clin Epidemiol. 2014; 67: 1309-19.

Medicine or the Law… It is all a Question of Probability

The CLAHRC WM Director was recently sent a transcript of the Richard Davies QC Memorial Lecture 2015, “Standards of Proof in Law and Science: Distinctions without a Difference“. The transcript was dispatched by Dr Martin Quinn, an old friend from his gynaecology days, and the speech was given by prominent High Court Judge, Justice Jay. Such experience as the CLAHRC WM Director has of High Court Judges is that they are a cognitively astute bunch, but not necessarily highly numerate. If he is right about that, then Jay is something of an exception. His theme was the similarity and differences between the scientific and legal intellectual frameworks. He first makes a parody of their different epistemologies, but soon comes round to cogent arguments that they are more united by their similarities than divided by their differences. After all they both have to analyse evidence, work out what it means, and make judgements under uncertainty. Individual cases come down to probabilities in both areas; the balance of probabilities in cases of tort, probabilities sufficient to put the matter ‘beyond reasonable doubt’ in criminal cases, and the relative probabilities of benefit and harm in medical cases.

This all means that both professions need quite sophisticated notions of probability with which to work. Doctors fall over their feet on probability, but Justice Jay has a clear understanding of frequentist and Bayesian notions of probability. As CLAHRC WM News Blog readers know only too well, frequentist statistics cannot tell you the probability that something is true (given the data), but only the probability of the data, given that something (typically the null hypothesis) is true. Given only the latter (i.e. a frequentist calculation of the probability of the data under a null hypothesis), then the probability of some alternative hypothesis can only be calculated given a prior probability. This is obviously a crucial concept in both law and medicine.

Consider two scenarios – a judge deciding on a case of homicide, and a doctor considering the diagnosis of Duchenne muscular dystrophy. The judge has blood group  and inconclusive alibi – what was the probability that the accused was at the scene of the crime? The doctor has the result of a blood enzyme test and family history – what is the probability of the diagnosis? They both use Bayes theorem:

052 DCB - Judge Doctor Fig 1

The difference between judge and doctor lies not in the axiomatic method that normatively underpins the requisite probabilities, but what to do with them. The judge interprets a given probability with reference to a legal framework – reasonable doubt. That might correspond to posterior odds of, say, 99:1. The doctor must make his interpretation with reference to the balance of benefits and harms. Since benefits and harms are not all equivalent, the decision turns on a ‘loss function’. The loss function is derived under expected utility theory and weights probabilities by preferences.[1]

Both doctors and lawyers must understand notions of contingent probability. Failure to understand this idea leads to erroneous thinking, for example the famous ‘prosecutor’s fallacy’. This is exemplified in the case of Sally Clark, where an expert, Roy Meadow, argued that guilt was likely on the grounds that two cases of infant death in one family are very rare; one in many thousands. However, that consideration of the frequency of a certain scenario is quite beside the point once the scenario has been observed. In that case, the salient probability is a contingent probability – namely that of malfeasance versus that of natural causes given the observed outcome.

Cases of tort frequently turn on evidence of effectiveness. For example, the observed relative risk reduction in a meta-analysis of high quality RCTs may observe a statistically ‘significant’ 55% reduction in relative risk of outcome x if treatment y was administered. Given a particular case of tort where failure to administer y (in the absence of a contra-indication) was followed by x, it might be tempting to argue that causality can be established on the balance of probabilities. But not so fast:

  1. This is to conflate the probability of the effect and the probability of the data, and ‘well-brought-up people do not do that’; the prior must be brought into play.
  2. As in medical care, the particular features of the case must be taken into account – there may be good grounds to argue that the typical effect would be greater or smaller among people resembling the case under consideration.

In the end, ‘evidence-based medicine’ may have relatively little effect on outcomes in cases under tort. This is because most interventions examined by RCTs, the standard tool of evidence-based medicine, are not so powerful as to halve relative risks – relative risk ratios of around 20% are more typical. Furthermore, the magnitude of effect is generally smaller for less serious outcomes (such as admission to hospital with angina) than for more serious outcomes (such as cardiac death) that drive compensation quanta in claims.[2] The situation is different with diagnostic errors, procedural errors, and failure to rescue. The CLAHRC WM Director favours a change in the law, whereby compensation is weighted by the (Bayesian) probability of causality rather than the (illogical?) balance threshold.

— Richard Lilford, CLAHRC WM Director

References:

  1. Thornton JG, Lilford RJ, Johnson N. Decision analysis in medicine. BMJ. 1992 ; 304(6834): 1099-103.
  2. Bowater RJ, Hartley LC, Lilford RJ. Are cardiovascular trial results systematically different between North America and Europe? A study based on intra-meta-analysis comparisons. Arch Cardiovasc Dis. 2015; 108(1): 23-38.

CHWonomics

Watching NoCounter interact with “Aunty” Martha (not their real names) in Mahwaqe, South Africa, and learning about NoCounter’s roles as Martha’s health advocate, personal trainer and medication manager was anything but dismal. So as a dismal scientist, I was fascinated by how Community Health Workers (CHWs) seem to contradict one of our most famous founders, Adam Smith. To help explain one of the concepts for which he would become famous, “the invisible hand”, Smith wrote: “I have never known much good done by those who affected to trade for the public good”.[1]

To consider whether NoCounter and other CHWs are an exception to this statement, there are three questions that need to be considered:

Is the CHW doing good?
Almost all of the available research evidence suggests that CHWs are effective in enhancing the health of their communities,[2] and since the World Health Organization also see CHWs as playing a pivotal role in helping countries achieve health-related Millennium Development Goals,[3] it is most likely that CHWs are “doing good”. In Mahwaqe, we saw how NoCounter helped Martha do the chair yoga exercises that now mean she can walk and explained her medications, which helped Martha understand the importance of adherence.

Is the CHW trading?
NoCounter is giving up her time (working around 50% FTE) and in return, receives a stipend from an NGO of around R800 (~£36) per month and as such, is trading. However, as a maid in South Africa, she could earn around R1,200 (~£54) per month for the same hours, so NoCounter does not seem to be receiving the full monetary value of her time. If approximate role equivalence can be assumed, compared to a CHW in the US, NoCounter’s time is undervalued by a factor of around 8.5: a US CHW working for an hour could buy 3.3 McDonald’s Big Macs; NoCounter could buy 0.4.[4] [5] NoCounter is also using her skills and experience to provide care, but economics would describe these as “non-rivalrous” and thus not directly tradable.

Is the CHW doing so for the public good or her own self-interest?
Adam Smith might be confused by NoCounter, because her aim doesn’t seem to be wealth maximisation. However, a “utility maximising” economist would argue that NoCounter is making up for not being paid the full monetary value of her time by obtaining utility either from substitutes for money or from directly helping her community.[6] Even if NoCounter obtains utility from the latter, her motivation would still be to do public good. With regards to money substitutes, CHWs may also receive non-monetary incentives such as community respect, housing and access to health care and/or be motivated in their roles via the support of their families.[6] [7] Furthermore, the CHW role is particularly desirable in areas where residents have a high marginal rate of substitution for leisure over consumption, since CHWs do not have to commute to their place of work. Finally, a by-product of NoCounter’s work as a CHW from which she benefits directly is that she lives in a healthier community: by encouraging vaccination of new-borns, for example, she is lowering her own risk of TB.

On this last question, the relative importance of the different reasons why CHWs undertake their role for a wage lower than they appear to be worth, we cannot be certain about the answer. Research in this area is critical given the push to eliminate the under-supply of CHWs.[8] There are also additional pre-conditions – the organisational structure required to implement a successful CHW programme [9] – that also must be met before the demand for CHWs can be realised (made “effective”) in practice. Nevertheless, it is critical to determine whether all of the additional CHWs required to meet demand would also offer their labour at a low relative price. This was assumed in a costing exercise of a CHW roll-out programme,[10] but which prima facie contradicts basic economic theory of demand and supply.

Fortunately for me, economics provides one approach to studying the interaction between monetary and non-monetary incentives with respect to the supply of labour, for example using discrete choice experiments, where CHWs would be asked to make a choice between a series of pairs of packages of stipend/salary, level of health produced, and non-monetary incentives (see [11] for an example). Such experiments would need to be repeated in (and possibly also within) different countries, since the relative value of “doing good” by volunteering may well differ according to a country’s stage in economic development. Such work would help to provide evidence regarding the sustainability of CHWs as a cadre of health care providers. Here, we hypothesise a U-shaped curve if propensity to volunteer is plotted against GDP per capita

— Celia Taylor, Senior Lecturer

References:

  1. Smith A. An Inquiry into the Nature and Causes of the Wealth of Nations. London: Strahan and Cadell, 1776.
  2. Perry H, Zulliger R. How Effective are Community Health Workers? An Overview of Current Evidence with Recommendations for Strengthening Community Health Worker Programs to Accelerate Progress in Achieving the Health-related Millennium Development Goals. Baltimore, MD: John Hopkins Bloomberg School of Public Health, 2012.
  3. World Health Organization and Global Health Workforce Alliance. Global Consultation on Community Health Workers. Geneva, Switzerland: World Health Organization, 2010.
  4. Payscale Homepage. 2015.
  5. The Economist. The Big Mac Index. 2015.
  6. Greenspan JA, McMahon SA, Chebet JJ, Mpunga M, Urassa DP, Winch PJ. Sources of community health worker motivation: a qualitative study in Morogoro Region, Tanzania. Hum Resour Health. 2013; 11: 52.
  7. Dambisya YM. A review of non-financial incentives for health worker retention in east and southern Africa. In: EQUINET Discussion Paper Number 44 with ESCA-HC. Loewenson R (Editor). Harare, Zimbabwe: EQUINET, 2007.
  8. One Million Community Health Workers Campaign. One Million Community Health Workers Campaign. 2015.
  9. World Health Organization, Policy Brief. Community health workers: What do we know about them? Geneva, Switzerland: World Health Organization, 2007
  10. McCord GC, Liu A, Singh P. Deployment of community health workers across rural sub-Saharan Africa: financial considerations and operational assumptions. Bull World Health Organ. 2012; 91(4):244-53B.
  11. Kasteng F, Settumba S, Källander K, Vassall, A, inSCALE Study Group. Valuing the work of unpaid community health workers and exploring the incentives to volunteering in rural Africa. Health Policy Plan. 2016: 31(2): 205-16.

Three Ways in Which a Treatment Effect may be Masked in Policy / Service Delivery Studies

Contamination may mask the effectiveness of an intervention in an otherwise perfectly conducted trial. But what is contamination? The CLAHRC WM Director proposes that we do away with this moniker and instead use the term ‘masking’ to describe circumstances where improvement in the control groups vitiates a positive result. He further proposes that ‘masking’ be categorised into three classes:

  1. Mimetic masking, where the control group becomes aware of the study intervention and implements it.
  2. Neighbourhood masking, where the control group benefits from any benefit in the intervention group through the ‘neighbourhood effect’. Herd immunity is an obvious example. Another recent example relates to the effects of deworming, which reduces the load of eggs that may infect others.[1] In both cases the controls, rather than being ‘contaminated’ are ‘de-contaminated’!
  3. Temporal masking, where the intervention, or another effective intervention, comes into vogue quite independently of the study and hence where headroom for further gains in the intervention group are diminished. This has also been referred to as a ‘rising tide phenomenon’.[2]

Trials may be conceptualised as having pragmatic or aetiological motivations.[3] Categories 1 and 2 undermine both the pragmatic and aetiological motivations for a trial. Category 3 undermines only aetiological. Category 2, and to a variable extent 1, may be avoided by cluster studies, but this does not mitigate Category 3 masking.

052 DC - Three Ways Fig1-1

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Miguel E, & Kremer M. Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities. Econometrica. 2004; 72(1): 159-217.
  2. Chen YF, Hemming K, Stevens AJ, Lilford RJ. Secular trends and evaluation of complex interventions: the rising tide phenomenon. BMJ Qual Saf. 2015. [ePub].
  3. Schwartz D, & Lellouch J. Explanatory and pragmatic attitudes in therapeutical trials. J Chronic Dis. 1967; 20: 637–48.

Are People Who Are Deeply Religious More Altruistic Than Secular Controls?

CLAHRC WM takes a large interest in the clinician-patient relationship – the Director has a special interest in the doctor-patient relationship. Moreover, CLAHRC WM has developed a research protocol (led by Prof Julian Bion) to evaluate methods to augment compassion in acute medical care. But the doctors and clinicians are under broader influences than their immediate work environment and their post-professional education. Despite a similar education and environment some give much more of themselves than others. There are broader personal and cultural influences at work. So one may suppose that doctors who are very religious might give more than their secular peers. Well, any research on that lies in the future. But as far as human beings as a whole are concerned, the Economist provides a synopsis [1] of a fascinating study.[2] They studied altruistic responses using a variant of the well-studied Dictator Game, which is a validated test of altruism. The investigators interviewed 1,170 children, one per family, across six countries. About half of the families turned out to be religious, and half of these were ‘highly observant’. So were the children of pious families more altruistic than their peers? Were they equally altruistic? Could it be that they were less altruistic? Well it turned out that children of non-religious families were more altruistic than their peers. What’s going on here? Is there a flaw in the study? If not, how can the results be explained? The CLAHRC WM Director is surprised by this result and has no answer to these questions.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. The Economist. Matthew 22:39. The Economist. 07 Nov 2015.
  2. Decety J, Cowell JM, Lee K, et al. The Negative Association between Religiousness and Children’s Altruism across the World. Curr Biol. 2015; 25(22): 2951-5.

Step Wedge Cluster Design for Service Delivery Interventions Comes to New England Journal of Medicine

Dreischulte and colleagues randomised 34 practices in clusters using a step-wedge design, to evaluate a complex intervention to reduce prescription errors in primary care.[1] The intervention included an educational component and informatics (as in the famous PINCER trial [2]), but also included a financial incentive. There was a marked drop in the types of high-risk prescribing targeted in the intervention. In addition, admissions decreased in the hypothesised direction. Adjustments were made for intra-class correlations at various time points, but what about temporal trends? Is this the first step wedge cluster study in the world’s top medical journal?

— Richard Lilford, CLAHRC WM Director

References:

  1. Dreischulte T, Donnan P, Grant A, et al. Safer Prescribing – A Trial of Education, Informatics, and Financial Incentives. New Engl J Med. 2016; 374(11): 1053-64.
  2. Hemming K, Chilton PJ, Lilford RJ, Avery A, Sheikh A. Bayesian cohort and cross-sectional analyses of the PINCER trial: a pharmacist-led intervention to reduce medication errors in primary care. PLoS One. 2012; 7(6): e38306.

Sugar Taxes in Mexico

Consumption of sugar dropped in Mexico after implementation of a sugar tax.[1] It was falling already, but there was a step down, albeit a small one, compared to a counterfactual created by extrapolating previous trends. Demand was most elastic in the lowest income group. Sugar taxes are a tad illiberal, but maybe they can be accepted on the basis that both consumers and the industry (as a whole) would like sugar levels in merchandise to drop. But each individual company is terrified that they will lose market share to competitors if they make the first move. Taxes apply to all products and so keep the playing field level. Some have suggested that taxes have to be quite high (about 20% of the purchase price) to have any material effect, but the effect in Mexico was achieved at levels of about half of this. This week the Chancellor of the Exchequer brought in a sugar tax in the UK. We should track the effects on sugar consumption. Pure, white and deadly, we have done numerous posts on the danger of free sucrose (for example, [2] [3]).

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Colchero MA, Popkin BM, Rivera JA, Ng SW. Beverage purchases from stores in Mexico under the excise tax on sugar sweetened beverages: observational study. BMJ. 2016; 352: h6704.
  2. Lilford R. How Much Sugar is too Much? CLAHRC WM News Blog. 25 September 2015.
  3. Lilford R. More, Yet More, On Pure, White and Deadly. CLAHRC WM News Blog. 31 July 2015.