Tag Archives: Finance

Living on Less Than One Dollar per Day

Sam Watson recently drew my attention to this fascinating article by my heroes – Adhijit Banerjee and Esther Duflo.[1] How do people in the “bottom billion” spend an income of around $1 per day? The authors turn to household surveys covering 13 countries in Asia, Africa and Central America (one assembled by the World Bank, and the others by the RAND Corporation). Even though it is hard to get a full stomach on $1 per day and many are hungry, not all money is spent on food – the proportion varies from a half to three-quarters of income spent on food. Nor are the cheapest foods always selected – taste crowds out Calories, even if that leaves you hungry. The second largest source of expenditure is festivals, such as weddings and funerals. Radios are a priority and show elastic demand on income. There is an inverse relationship across countries between spending on radios and on festivals. Asset ownership is very low – even in rural areas bicycle ownership is low – at a third of households or less. Education attracts a very low proportion of expenditure; 2-3% of the household budget in Pakistan, for example. People often feel hungry, many are anaemic, and energy levels are low. Illness rates are high and anxiety common when compared to high income countries. I guess many are in a poverty trap and need a little help to get them out of it, but the results resonate with the Gospel of Matthew, ‘Man shall not live by bread alone’.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Banerjee AV & Duflo E. The Economic Lives of the Poor. J Econ Perspect. 2007; 21(1): 141-67.

The ‘Robin Hood’ Hypothesis in 33 African Countries

Across low- and middle-income countries (LMICs), over 50% of total health care spending is derived from out-of-pocket expenses. Some of these are formal recognised tariffs in public health systems. However, a proportion are irregular or informal payments (bribes/kick-backs). It is hypothesised that these informal payments are used to subsidise the poor at the expense of the rich after the fashion of Robin Hood in English folklore. Enter results from a series of publically available repeated surveys called ‘Afrobarometer‘. Here public attitudes and experiences relating to democracy and governance are surveyed in 18 African counties. Nationally representative samples of over 25,000 individuals are selected randomly across participating countries. Afrobarometer provides the data for an important study [1] of the extent to which informal payments were elicited across people of different income levels (according to the Lived Poverty Index). Far from confirming the Robin Hood hypothesis, the authors find a higher occurrence of bribe paying among the poorest people across the countries studied – elasticity is negative in that the richer the person, the lower the probability that they will have paid a bribe on attending a health care facility. These results are similar to those obtained in a previous study in Hungary. There is some evidence that the problem is worse in cities where service providers are less likely to have known or have community affiliations with patients. This finding reminds me of the Bible scripture – “For whosoever hath, to him shall be given… but whosoever hath not, from him shall be taken away...” (Matthew 13:12).

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Kankeu HT, Ventelou B. Socioeconomic inequalities in informal payments for health care: An assessment of the ‘Robin Hood’ hypothesis in 33 African countries. Soc Sci Med. 2016; 151: 173-86.

More on Bribes to Access Health Care

Further to the previous study,[1] Kankeu et al. also studied how supply-side factors affect informal payments.[2] In contrast to the previous study across multiple diseases and countries, this study concerns itself with one disease (HIV) and one country (Cameroon). The study was built on a naturally representative survey among people with HIV/AIDs. Like the previous paper, this study concerns not all out-of-pocket payments, but specifically payments above stipulated tariffs – i.e. the focus on informal payments / bribes. The Global Corruption Barometer, published by Transparency International, shows that across all conditions, a third of people who visit a health care facility in Cameroon pay a bribe. As it turns out, bribes are less often exacted from HIV payments. Supply-side factors seem to have a large influenza on bribe payment in HIV patients in the study; 12% in private for-profit facilities, 3% in public, and under 1% in private not-for-profit. The actual amount paid when a bribe is elicited is highest in urban areas and in private for-profit facilities. Importantly, facilities where more than one care provider carries out all tasks required for a given patient have a higher probability of eliciting bribes than those where provision is spread among more than one person. The authors caution that increasing salaries might not reduce bribe taking (rent seeking) and may actually increase in line with a previous News Blog report.[3] In both papers the authors recommend National Insurance systems, and in this particular paper the authors fancy performance-related pay, despite its caveats.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Kankeu HT, Ventelou B. Socioeconomic inequalities in informal payments for health care: An assessment of the ‘Robin Hood’ hypothesis in 33 African countries. Soc Sci Med. 2016; 151: 173-86.
  2. Kankeu HT, Boyer S, Toukam RF, Abu-Zaineh M. How do supply-side factors influence informal payments for healthcare? The case of HIV patients in Cameroon. Int J Health Plann Manage. 2016; 31(1): E41-57.
  3. Lilford RJ. Improving Health Care From Outside Organisations. NIHR CLAHRC West Midlands News Blog. October 14 2016.

More on Free Goods and Aid Dependency

In a previous News Blog we reported results showing that maintenance of communal lavatories was worse among people who had had a subsidy for lavatory maintenance withdrawn than among those who had never had the subsidy in the first place.[1] The ‘free good’ idea was at work here, whereby people can develop a sense of entitlement. A recent study involved providing free shoes for poor people.[2] Those who received the free shoes were more likely to feel that other people should provide family needs in general than those who had not been given free shoes – classic aid dependency. Handing out free shoes did not increase overall ownership of shoes, foot health or self-esteem; presumably because of ‘fungability’ – people used their shoe money for other purposes. Yet not all ‘free goods’ are bad – a recent paper co-authored by CLAHRC WM researchers showed that even very small user fees reduce access to services in Malawi and this hits the most vulnerable – children – the hardest.[3] Taken in the round this leads to the CLAHRC WM Director’s axiom – “ration health care, but not access to health care.”

— Richard Lilford, CLAHRC WM Director

References:

  1. Garn JV, Sclar GD, Freeman MC, et al. The impact of sanitation interventions on latrine coverage and latrine use: A systematic review and meta-analysis. Int J Hyg Environ Health. 2016. [ePub].
  2. Wydick B, Katz E, Calvo F, Gutierrez F, Janet B. Shoeing the Children: The Impact of the TOMS Shoe Donation Program in Rural El Salvador. World Bank Econ Rev. 2016.
  3. Watson SI, Wroe EB, Dunbar EL, et al. 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.

Unintended Consequences of Pay-For-Performance Based on Readmissions

Introducing fines for readmission rates crossing a certain threshold has been associated with reduced readmissions. Distilling a rather wordy commentary by Friebel and Steventon,[1] there are problems with the policy since it might not lead to optimal care:

  1. The link between quality of care and readmission is not good according to most studies, so that there is a risk that patients who need readmission will not get it.
  2. In support of the above, less than a third of readmissions are for the condition that caused the previous admission (which is not to say that none are preventable, but it suggests that a high proportion might not be).
  3. Risk-adjustment is at best imperfect.
  4. And this probably explains why ‘safety net’ hospitals caring for the poorest clientele come off worst under the pay-for-performance system.

I refer it my iron law of incentives – ‘only use them when providers truly believe that the target of the incentive lies within their control.’

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Friebel R, Steventon A. The multiple aims of pay-for-performance and the risk of unintended consequences. BMJ Qual Saf. 2016.

Do Cash Transfers to the Poor Encourage Feckless Behaviour?

In a brilliant working paper, Evans and Popova consider whether non-conditional cash transfers encourage people in low-income countries to increase their use of ‘temptation goods’, such as tobacco and alcohol.[1] Their systematic review found 19 studies. The answer to the question is ‘no’, there is no positive effect on consumption of temptation goods. This effect is confirmed if the analysis is confined to randomised trials. In fact the point estimate signifies lower consumption of the temptation goods in association cash transfers. The extra money provided by the cash transfers seems to be wisely invested, for example, in childhood education. Of course, this does not mean that there are no instances where someone (usually a man I am afraid) took money (which is usually given to a woman) in order to go drinking. But then, it is a poor heart that never rejoices!

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Evans DK, Popova A. Cash Transfers and Temptation Goods. University of Chicago, IL. 2016.

Accountable Care Organisations

Accountable Care Organisations have been introduced in many settings in the USA. Evaluations are few and far between, but a recent overview [1] finds that while they do not save money, they are associated with improved processes of care (e.g. increased rates of cancer screening), and intermediate outcomes (e.g. HbA1c and blood pressure in people with diabetes). Attempts to create something similar in the UK by merging hospital and primary care budgets are underway in England, notably in Manchester. Before and after studies, such as those typically used in evaluations, are prone to exaggerate effectiveness of quality improvement initiatives,[2] thanks to the rising tide phenomenon.[3] Moreover, merging budgets is not the only way to improve coordination of care across providers, as discussed in a previous post.[4] That said, merged budgets do align provider financial incentives with patient need and core professional values, and we have not reached the end of history on this topic – not nearly.

— Richard Lilford, CLAHRC WM Director

References:

  1. Song Z, Fisher ES. The ACO Experiment in Infancy – Looking Back and Looking Forward. JAMA. 2016; 316(7): 705-6.
  2. Eccles M, Grimshaw J, Campbell M, Ramsay C. Research designs for studies evaluating the effectiveness of change and improvement strategies. Qual Saf Health Care. 2003; 12: 47-52.
  3. Chen YF, Hemming K, Stevens AJ, Lilford RJ. Secular trends and evaluation of complex interventions: the rising tide phenomenon. BMJ Qual Saf. 2015. [ePub].
  4. Lilford RJ. Polycentric Organisations. NIHR CLAHRC West Midlands. 25 July 2014.

Another Study of Pay for Performance in Hospitals

It is such an attractive idea isn’t it. Pay more to hospitals that save more lives and penalise those that do not. Well that is exactly what the Centers for Medicare and Medicaid Services has been doing in the USA for the past few years with respect to just three medical conditions: myocardial infarction (heart attack), pneumonia, and heart failure. A three-year follow up study has now been reported comparing 2,919 participating hospitals with 1,348 control hospitals – there are a lot of hospitals in the US.[1] The main comparisons: 1) intervention vs. control hospitals; and 2) three targeted conditions vs. other conditions in the intervention hospitals. No effects were observed; intervention hospitals did no better than controls and, across interventions hospitals, the targeted conditions found no better than those that were not targeted. This finding is different to the short, but not long-term, results of a study in England,[2] [3] though this study was based on payment for compliance with process measures not outcome. The CLAHRC WM Director posits two reasons for the null result in the American study. First, mortality is insensitive to care quality.[4-6] Second, incentives work if ‘agents’ (people targeted by the incentive) think they can influence the outcome. So this is the CLAHRC WM Director’s theory – incentivise specific actions (i.e. process), not outcome, and never use hospital-wide mortality as a quality measure.

— Richard Lilford, CLAHRC WM Director

References:

  1. Figueroa JF, Tsugawa Y, Zheng T, Orav EJ, Jha AK. Association between the Value-Based Purchasing pay for performance program and patient mortality in US hospitals: observational study. BMJ. 2016; 353: i2214.
  2. Lindenauer PK, Remus D, Roman S, et al. Public reporting and pay for performance in hospital quality improvement. N Engl J Med. 2007; 356: 486-96.
  3. Kristensen SR, Meacock R, Turner AJ, et al. Long-term effect of hospital pay for performance on mortality in England. N Engl J Med. 2014; 371: 540-8.
  4. Girling AJ, Hofer TP, Wu J, Chilton PJ, et al. Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study. BMJ Qual Saf. 2012; 21(12): 1052-6.
  5. Lilford R, Mohammed MA, Spiegelhalter D, Thomson R. Use and misuse of process and outcome data in managing performance of acture medical care: avoiding institutional stigma. Lancet. 2004; 363: 1147-54.
  6. Lilford R, Pronovost P. Using hospital mortality rates to judge hospital performance: a bad idea that just won’t go away. BMJ. 2010; 340: c2016.

Getting Evidence into Practice

In the early days of CLAHRCs, ‘getting evidence into practice‘ was an important objective. We set about closing the T2 gap and used implementation science to get doctors to prescribe evidence-based care, dentists to use tooth protecting resins, and nurses to make regular observations. That is to say, we were concerned with how to make practitioners comply with standards over which they had complete jurisdiction. Theories of individual behaviour change were invoked, and rather then choose a theory on the basis of its impressive sounding title (e.g. prospect theory, social network theory), a framework was developed to identify barriers and facilitators of change.[1]

But practitioners increasingly follow the evidence when it is compelling and when the evidence-based standard is in their gift.[2] So, the big (and much more interesting) problem now is how to change the service in a generic way rather than simply to increase performance on a specific measure – we are becoming more concerned with draining the swamp than zapping individual mosquitoes.[3] In our CLAHRC we recently evaluated a compound (multi-component) intervention to improve home dialysis rates, having promulgated a guideline supporting improved access to such a service. We showed that agreement with the proposed change among stakeholders, an agreed

Implementation plan, managerial support, and product champions all facilitated the success of the intervention in taking West Midlands from the worst to the best performing region in England. However, the king of all intervention components was a financial incentive.[4] Fulop and colleagues have now published a similar multi-methods evaluation of an arguably even more complex intervention to improve access to acute stroke care.[5] The findings are very similar, save that we found more emphasis on financial incentives and also more problems in communication with patients; something that would perhaps not stand out in the hyper acute stroke context. The Fulop paper is an advance on ours in (at least) two respects. First, they compare and contrast across two regions/CLAHRCs and I always think controls should be used if possible; even one is better than none. Second, they illustrate the causal model with diagrams that make the theoretical framework they are using clear, a practice that is helpful in communicating the very real distinctions between the intervention as planned, its implementation/adaption, its upstream effects (e.g. staff knowledge/morale), its downstream effects (at the patient ‘level’), and the context in which all takes place.[6] People muddle these concepts and hence fall over their feet , but Fulop and colleagues have shown themselves to be sure-footed!

— Richard Lilford,

References:

  1. Michie S, van Stralen M, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventionsImplement Sci. 2011; 6: 42.
  2. Johnson N, Sutton J, Thornton JG, Lilford RJ, Johnson VA, Peel KR. Decision analysis for best management of mildly dyskaryotic smear. Lancet. 1993;342(8863):91-6
  3. 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 pointsBMJ. 2010; 341: c4413.
  4. Combes G, Allen K, Sein K, Girling A, Lilford R. Taking hospital treatments home: a mixed methods case study looking at the barriers and success factors for home dialysis treatment and the influence of a target on uptake ratesImplement Sci. 2015; 10: 148.
  5. Fulop NJ, Ramsay AIG, Perry C, et al. Explaining outcomes in major system change: a qualitative study of implementing centralised acute stroke services in two large metropolitan regions in England. Implement Sci. 2016; 11: 80.
  6. 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.

Relative Wealth and Health

It has been known since the time of Condorcet, over 200 years ago, that poverty is bad for you – an income effect.[1] Studies have also shown an association between relative poverty and life expectancy – a relative income effect.[2] It has become common to interpret these associations as evidence that relative poverty causes poor health, net of absolute wealth. Previous studies are trumped by the largest association study ever conducted, based on nearly 1.4 trillion person years of observation and 1.4 billion de-identified tax records across the United States of America.[3] Beat that! So, what did it find?

  1. The association between wealth and longevity (the income effect) is confirmed. The difference in male life expectancy at age 40 differs by a colossal 15 years between the top and bottom 1% on the income scale. Interestingly, there is no threshold above which the association fades; rather the reverse.
  2. Inequality has increased in recent years because life expectancy has increased faster among people on high incomes than among those on low incomes.
  3. Differences in mortality adjusted for race and ethnicity and net of income varies by geographic area. In other words, the differences in life expectancy between rich and poor itself differs by geographic area.
  4. Differences in income effect by area are mostly explained by differences in health behaviour (rather than, for example, access to healthcare).

The finding that different areas have very different survival rates net of income, allows the effect of numerous other variables on the income effects (absolute and relative) to be explored. Some places have large gradients in wealth, others smaller. The existence of a relative income effect is confirmed by a negative correlation between inequality (measured by the Gini co-efficient) and longevity. But this is an artefact of the concave nature of the relationship between income and life expectancy. In fact, among the lowest quartile by income there is no correlation between wealth disparity and health, whereas, ironically, it is strongest among the upper quartile. So the idea that it is the poor who exhibit the strongest relative income effect is completely wrong. In fact, poor people have healthier behaviours and live longer when they live in rich cities alongside a highly-educated, high income populations than in poorer cities with ‘better’ Gini co-efficients. Perhaps this is because rich cities can raise more in tax at a given tax rate. Slum formation is more rapid in cities with a high Gini co-efficient. This is sometimes interpreted as a high Gini causing poverty, rather than the more plausible interpretation that rational internal migrants gravitate to richer cities. These data are important because they call into doubt the simplistic idea that all we have to do to right the world’s wrongs is to tax rich people more heavily.

— Richard Lilford, CLAHRC WM Director

References:

  1. Benzeval M, Bond L, Campbell M, et al. How Does Money Influence Health? York: Joseph Rowntree Foundation. 2014.
  2. Wilkinson RG, Pickett KE. Income inequality and population health: a review and explanation of the evidence. Soc Sci Med. 2006; 62(7): 1768-84.
  3. Chetty R, Stepner M, Abraham S, et al. The Association Between Income and Life Expectancy in the United States, 2001-2014. JAMA. 2016; 315(6):1750-66.