Tag Archives: Finance

Pre-Payment Systems for Access to Healthcare

In a previous news blog I explicated a theory for improved access to services in countries of moderate income.[1] I argued that services would always be limited in very low-income countries. On the other hand, in high-income countries the tax base is sufficient to support comprehensive public services, including health services. However, I explained that in countries of intermediate wealth, the tax base was often immature and small, relative to the total economy. In such a scenario it is necessary to rely on community contributions in order to improve access to services. I refer to this as the IKEA model.

I found a very nice example of the IKEA model recently. This is a pre-payment service implemented by Safaricom and other companies in Kenya called M-TIBA.[2] These organisations provide an electronic wallet that families can contribute to, which provides ready cash at the point when services are required. Such a service can ensure that money is available for out-of-pocket payments. Electronic pre-payment thus acts as a kind of community insurance or risk-sharing system.

I see great potential in these services, but feel that they should be evaluated. Like microfinance, people may make exaggerated claims of how beneficial they can be.[3] I expect that these services will be most useful when they are all linked to other services. Thus, I imagine that a pre-payment system will be much more effective in securing transport to hospital when it has grown up in parallel with an inexpensive (perhaps partially subsidised) local ambulance service. As part of my work as lead for the Access work package in the NIHR Global Health Research Unit on Global Surgery, I shall be investigating this type of system in improving access to acute surgical and medical care.

— Richard Lilford, CLAHRC WM Director

References:

    1. Lilford RJ. Towards a Unifying Theory for the Development of Health and Social Services as the Economy Develops in Countries. NIHR CLAHRC West Midlands News Blog. 13 October 2017.
    2. PharmAccess. “M-TIBA is truly leapfrogging healthcare in Kenya”. 15 December 2015.
    3. Banerjee A, Karlan D, Zinman J. Six Randomized Evaluations of Microcredit: Introduction and Further Steps. Am Econ J Appl Econ. 2015; 7(1): 1-21.

 

Advertisements

The Affordability of Care – Hard to Measure but Increasingly Important

Traditionally epidemiologists who worked on the relationship between wealth and disease were concerned with the effect of the first on the second. But, of course, disease can affect wealth, and economists like Jeffrey Sachs spotted the resulting circularity: poverty -> disease -> more poverty -> more disease. Increasingly, clinicians have started to worry about the catastrophic costs of disease and my colleague Bertie Squire from Liverpool School of Tropical Medicine is searching for treatment pathways to mitigate the financial consequences of recurrent tuberculosis. The Oregon experiment, reported in your News Blog,[1] shows that the most obvious benefit from extending insurance coverage to the un-insured lies in reducing the incidence of catastrophic loss.

Catastrophic loss:Events whose consequences are extremely harsh in their severity, relating to one or more losses such as bankruptcy, total loss of assets, or loss of life.” (The Law Dictionary, 2017).

An important question then, is how generous can publically financed insurance be? Or, to put the question another way, how can the affordability of health care be measured? This is a rather different question to that of the affordability of a particular new technology – a question of its Incremental Cost Effectiveness Ratios. This is because HTA is designed to determine the upper bound on ‘affordability’, while the fiscal question of affordability as a whole is concerned with total expenditure.

A paper in a recent issue of JAMA proposes an approach based on the total health costs divided by the median household income.[2] This might be a useful rule of thumb, but it is beset by problems, as pointed out in two leading articles.[3] [4] One such problem arises from the observation that some of the costs of health care / insurance premiums likely come out of household incomes – companies would probably pay employees more if it were not for the insurance premiums – so there is some double counting going on. More fundamentally, affordability cannot be inferred simply by the proportion of expenditure going on health care. One could argue, for instance, that the richer the country (higher the per capita GDP), the greater should be the expenditure on health. One way to get at the affordability construct would be to examine the cost of health care as a proportion of money left over after subtracting the ‘essentials’ of housing, food, clothing and transport to and from school / work. Another would be to calculate the effect of health care costs on how many families tip over into bankruptcy or teeter on the edge thereof. Unaffordability would still vary by type of family and type of insurance system, especially in a variegated health system like that in the USA. A simple number, like proportion of GDP spent on health, can only give a very coarse-grained idea of the consequences of increasing or decreasing the proportion of resources dedicated to health care. It is also important to consider the effect of high health care costs on the broader economy. There is always a danger that, absent price signals, the allocation to health will exceed what can be justified in terms of the benefit realised. That is to say that, given information asymmetries, health care will be driven more by provider than consumer needs.

— Richard Lilford, CLAHRC WM Director

References:

  1. Baicker K, Taubman SL, Allen HL, et al. The Oregon Experiment – Effects of Medicaid on Clinical OutcomesN Engl J Med. 2013; 368: 1713-22.
  2. Emanuel EJ, Glickman A, Johnson D. Measuring the burden of health care costs on US families: the Affordability Index. JAMA. 2017; 318(19): 1863-4.
  3. Antos J, Capretta JC. Challenges in Measuring the Affordability of US Health Care. JAMA. 2017; 318(19): 1871-2.
  4. Reinhardt U. What Level of Health Spending Is “Affordable?” JAMA. 2017; 318(19): 1869-70.

Vaccination Savings

We know that vaccination is one of the most cost-effective interventions in terms of improving public health, but it can only be at its most effective if it is encouraged and supported by policy-makers and government officials. A recent paper in the Bulletin of the World Health Organization looked at the potential economic benefits of providing ten different vaccinations in 73 low- and middle-income countries.[1] These included vaccinations against hepatitis B, measles, rubella, and yellow fever. The authors found that if vaccinations were given routinely between 2001 and 2020, not only would 20 million children avoid death, but there would also be an estimated saving of $347 billion. This figure is predominantly made up of lifelong productivity gains from deaths avoided ($330 billion), but also from disabilities avoided ($9.4 billion), treatment costs ($4.5 billion), transport costs ($0.5 billion), and lost caregiver wages ($0.9 billion). Further they estimate that $820 billion would be saved from the broader economic and social value of vaccinations. The biggest contributor to these estimates was vaccination against measles, followed by H. influenza type b, S. pneumoniae, and hepatitis B.

— Peter Chilton, Research Fellow

Reference:

  1. Ozawa S, Clark S, Portnoy A, et al. Estimated economic impact of vaccinations in 73 low- and middle-income countries, 2001–2020. Bull World Health Organ. 2017

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.

Discontinuities in Data – a Neat Statistical Method to Detect Distorted Reporting in Response to Incentives

Discontinuities can be very revealing in Service Delivery and Policy Research – they provide a statistical method to detect the distorting effects of incentives. For example, the statistical test for p-hacking reported previously in your News Blog,[1] is based around the p<0.05 threshold for statistical significance. While the p-value is easily ‘hacked’ by selectively reporting ‘significant’ results, other data may be harder – death rates for example.

The great American economist Raymond Fisman (he of the New York traffic violations fame)[2] and Yongxiang Wang examined industrial deaths in China.[3] A threshold for such deaths was set at national level, with a penalty for Provincial administrations who failed to reach the target threshold. The distribution of deaths across provinces looked like this before the incentives went live:

085 DCii Discontinuities in Data Fig 1

After the incentive, it looked like this:

085 DCii Discontinuities in Data Fig 2

Not only that, but this discontinuity is found exclusively in reports from the fourth quarter of the year. This makes a compelling case – if you provide a target and managers do not think it is fair, then they will manipulate it, even if it is something that, on the face of it, is hard to manipulate. You would not succumb to such a temptation, do I hear you say? But you would, oh yes, you would!

— Richard Lilford, CLAHRC WM Director

References:

  1. Lilford RJ. Look out for ‘p-hacking’. NIHR CLAHRC West Midlands News Blog. 11 September 2015.
  2. Fisman R, Miguel E. Cultures of Corruption: Evidence from Diplomatic Parking Tickets. NBER Working Paper No. 12312. 2006.
  3. Fisman R & Wang Y. The Distortionary Effects of Incentives in Government: Evidence from China’s “Death Ceiling” Program. Am Econ J Appl Econ. 2017; 9(2): 202-18.

NICE Goes to the USA After All

Someone once said that you could always trust America to do the right thing – after they have tried everything else.[1] So at last, legislation has been passed in New York to implement value-based pricing for their States’ Medicaid programme (6m beneficiaries!). For the details, read an interesting article in JAMA.[2]

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Quote Investigator. Americans Will Always Do the Right Thing — After Exhausting All the Alternatives. 11 November 2012.
  2. Hwang TJ, Kesselheim AS, Sarpatwari A. Value-Based Pricing and State Reform of Prescription Drug Costs. JAMA. 2017; 318(7): 609-10.

Diet and Socioeconomic Status

People looking to lose weight and/or get healthy try a wide variety of diets, from fad diets with highly specific restrictions on what can be eaten, to general healthy eating plans. One such nutritional recommendation is the Mediterranean diet, based on the “food patterns typical of Crete… Greece and southern Italy…”,[1] and entails consumption of high amounts of plant foods (fruit, vegetables, cereals, legumes, etc.) and olive oil, moderate amounts of dairy, fish and wine, and low amounts of poultry and red meat. A number of observational studies have shown associations between such a diet and lower incidences of cardiovascular disease (CVD) and associated mortality, cancer, neuro-degenerative disorders, and overall mortality. However, there is uncertainty whether such benefits differ across different socioeconomic groups.

Bonaccio et al. carried out a prospective analysis of nearly 19,000 Italians to see the effect of the Mediterranean diet on CVD.[2] While there was an overall reduction in CVD risk associated with adherence to the diet (HR=0.85, 95% CI 0.73-0.99), this was not seen across all socioeconomic groups – only in those who were educated to a postgraduate or higher level (HR=0.43, 0.25-0.72) and in those with a high (>€40,000) household income (HR=0.39, 0.23-0.66). Those with less education (HR=0.94, 0.78-1.14) and lower income (HR=1.01, 0.79-1.29) had no significant association. Why such a difference? Subgroup analysis of people with similar adherence to the diet showed that there were a number of differences in the diet of those with high compared to low education, and those with high compared to low income. These included consumption of organic vegetables (which would have higher antioxidants and lower levels of pesticides), monounsaturated fatty acids (found in avocado, nuts, olives, etc.), micronutrients, and whole-grain bread, as well as greater dietary diversity.

So perhaps it is more important to make sure the food you are eating is of high quality and varied, than just simple healthy eating. Of course, access to high quality food of high nutritional value is not easy for poor people.

— Peter Chilton, Research Fellow

References:

  1. Willett WC, Sacks F, Trichopoulou A, Drescher G, Ferro-Luzzi A, Helsing E, Trichopoulos D. Mediterranean diet pyramid: a cultural model for healthy eating. Am J Clin Nutr. 1995; 61(6): 1402S–6S.
  2. Bonaccio M, Di Castelnuovo A, Pounis G, et al. High adherence to the Mediterranean diet is associated with cardiovascular protection in higher but not in lower socioeconomic groups: prospective findings from the Moli-sani study. Int J Epidemiol. 2017.

Declining Readmission Rates – Are They Associated with Increased Mortality?

I have always been a bit nihilistic about reducing readmission rates to hospitals.[1][2] However, I may have been overly pessimistic. A new study confirms that it is possible to reduce readmission rates by imposing financial incentives.[3] Importantly, this does not seem to have caused an increase in mortality – as might occur if hospitals were biased against re-admitting sick patients in order to avoid a financial penalty. “False null result” (type two error), do I hear you ask? Probably not, since the data are based on nearly seven million admissions. In fact, 30 day mortality rates were slightly lower among hospitals that reduced readmission rates.

— Richard Lilford, CLAHRC WM Director

References:

  1. Lilford RJ. If Not Preventable Deaths, Then What About Preventable Admissions? NIHR CLAHRC West Midlands News Blog. 6 May 2016.
  2. Lilford RJ. Unintended Consequences of Pay-For-Performance Based on Readmissions. NIHR CLAHRC West Midlands News Blog. 13 January 2017.
  3. Joynt KE, & Maddox TM. Readmissions Have Declined, and Mortality Has Not Increased. The Importance of Evaluating Unintended Consequences. JAMA. 2017; 318(3): 243-4.

Government vs. Private Schools

CLAHRC WM is not just interested in health care since the methods we use are equally relevant to decision-makers in education, social services, industrial policy, criminology, and so on. We should all be learning from each other. In a previous blog I reported on the (mostly positive) results of the ‘Moving to Opportunity’ experiment in the USA, where families were given an opportunity to move from a deprived neighbourhood to a more salubrious one. So I was interested to spot an RCTs of vouchers that allowed children (over a wide age range) from government schools to attend private schools (also in the USA).[1] The experiment was recent (last five years) and we have outcomes at one year only. Seventy percent of pupils allocated a voucher to attend a private school took up their offer; so both intention to treat and per protocol analyses are reported. The educational outcomes were lower in the intervention group, and were statistically significantly lower for mathematics. This negative effect was greater if the voucher was taken up than if it was not. The negative effect was greater if the child came from a school that was not rated as poor performing than if the previous school was rated satisfactory or good. The negative effect was greatest if the child was in elementary school, and non-significantly positive if they were already in high school.

What caused the negative effect on educational outcomes? Simply moving school does not seem to explain the results, since a proportion of control children moved school with little or no apparent effect. However, private schools provide less instructional time than government schools, especially in elementary school. Other studies have also noted negative effects of moving children to private school on educational outcomes in the short term. But it is far too early to declare the intervention a failure. There is a limit to how much an elementary school child can assimilate, and it is the long-term effects that are important. However, I was surprised by this result – educational interventions have a habit of producing results different to those intended. Full marks to the US Congress, which had the wisdom to evaluate its own policies. The UK Cabinet Office has published a document arguing for more RCTs of policy,[2] and I expect to be able to report the results of further RCTs of educational interventions in the News Blog.

— Richard Lilford, CLAHRC WM Director

References:

  1. Dynarski M, Rui N, Webber A, Gutmann B, Bachman M. Evaluation of the DC Opportunity Scholarship Program. Impacts After One Year. Alexandria, VA: Institute of Education Sciences, 2017.
  2. Haynes L, Service O, Goldacre B, Torgerson D. Test, Learn, Adapt: Developing Public Policy with Randomised Controlled Trials. London: UK Cabinet Office, 2012.

Private Consultations More Effective than Public Provision in Rural India

Doing work across high-income countries (CLAHRC WM) and lower income countries (CLAHRC model for Africa) provides interesting opportunities to compare and contrast. For example, our work on user fees in Malawi [1] mirrors that in high-income countries [2] – in both settings, relatively small increments in out-of-pocket expenses results in a large decrease in demand and does so indiscriminately (the severity of disease among those who access services is not shifted towards more serious cases). However, the effect of private versus public provision of health care is rather more nuanced.

News Blog readers are likely aware of the famous RAND study in the US.[3] People were randomised to receive their health care on a fee-for-service basis (‘privately’) vs. on a block contract basis (as in a public service). The results showed that fee-for-service provision resulted in more services being provided (interpreted as over-servicing), but that patients were more satisfied clients, compared to those experiencing public provision. Clinical quality was no different. In contrast, a study from rural India [4] found that private provision results in markedly improved quality compared to public provision, albeit with a degree of over-servicing.

The Indian study used ‘standardised patients’ (SPs) to measure the quality of care during consultations covering three clinical scenarios – angina, asthma and the parent of a child with dysentery. The care SPs received was scored against an ideal standard. Private providers spent more time/effort collecting the data essential for making a correct diagnosis, and were more likely to give treatment appropriate to the condition. First, they compared private providers with public providers and found that the former spent 30% more time gathering information from the SPs than the public providers. Moreover, the private providers were more likely to be present when the patient turned up for a consultation. There was a positive correlation between the magnitude of fees charged by private providers and time spent eliciting symptoms and signs, and the probability that the correct treatment would be provided. However, the private providers are often not doctors, so this result could reflect different professional mix, at least in part. To address this point, a second study was done whereby the same set of doctors were presented with the same clinical cases – a ‘dual sample’. The results were even starker, with doctors spending twice as long with each patient when seen privately.

Why were these results from rural India so different from the RAND study? The authors suggest that taking a careful history and examination is part of the culture for US doctors, and that they had reach a kind of asymptote, such that context made little difference to this aspect of their behaviour. Put another way, there was little headroom for an incentive system to drive up quality of care. However, in low-income settings where public provision is poorly motivated and regulated, fee-for-service provision drives up quality. The same seems to apply to education, where private provision was found to be of higher quality than public provision in low-income settings – see previous News Blog.[5]

However, it should be acknowledged that none of the available alternatives in rural India were good ones. For example, the probability of receiving the correct diagnosis varied across the private and public provider, but never exceeded 15%, while the rate of correct treatment varied from 21% to about 50%. Doctors were more likely than other providers to provide the correct diagnosis. A great deal of treatment was inappropriate. CLAHRC West Midlands’ partner organisation in global health is conducting a study of service provision in slums with a view to devising affordable models of improving health care.[6]

— Richard Lilford, CLAHRC WM Director

References:

  1. 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: 595.
  2. Carrin G & Hanvoravongchai P. Provider payments and patient charges as policy tools for cost-containment: How successful are they in high-income countries? Hum Resour Health. 2003; 1: 6.
  3. Brook RH, Ware JE, Rogers WH, et al. The effect of coinsurance on the health of adults. Results from the RAND Health Insurance Experiment. Santa Monica, CA: RAND Corporation, 1984.
  4. Das J, Holla A, Mohpal A, Muralidharan K. Quality and Accountability in Healthcare Delivery: Audit-Study Evidence from Primary Care in India . Am Econ Rev. 2016; 106(12): 3765-99.
  5. Lilford RJ. League Tables – Not Always Bad. NIHR CLAHRC West Midlands News Blog. 28 August 2015.
  6. Lilford RJ. Between Policy and Practice – the Importance of Health Service Research in Low- and Middle-Income Countries. NIHR CLAHRC West Midlands News Blog. 27 January 2017.