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Systems, Insurance, Money, and COVID-19

Your weekly, opinionated dose of health systems and economics fresh from the press.
Systems, Insurance, Money, and COVID-19

Happy new year everyone! The first week of the year saw my gaze drawn to a number of interesting papers, some dealing with COVID-19, some with less newsworthy, but more prosaic topics. One paper looks at the role of health systems in combating COVID-19, where the authors conclude that the only variable they could detect which mattered in the effectiveness of a health system's response was the number of hospital beds per capita. Another paper looks at pay for performance programmes in healthcare and their effect on health systems, healthcare providers, and consumers, while a third looks at the ways in which insurance providers use patient-reported data.

We then go on to a paper which discusses genomic detection for non-small-cell lung cancer before coming back to COVID-19. Out of the last four papers, two discuss food systems and information systems in the light of COVID-19, and the other two one discuss the impact of money on healthcare from the perspective of individuals. One looks at the impact of formal credit constraints on rural Chinese people, while the other looks at unexpected windfalls in the form of lottery winnings in Urban Singapore.

The Role of the Health System in Combating COVID-19

This paper by Bayraktar et. al. discusses the role of different health systems in dealing with COVID-19. The responses of different governments towards this unprecedented health crisis have been starkly variant. On one hand, you had China, which imposed draconian lockdowns and conducted mandatory testing on everyone in the city. On the other, you had the United States where the response was both patchwork and badly-coordinated. However, as the authors note:

When the countries with the highest COVID-19 cases ratio are considered, the highest death/case rates belong to France, Italy and the United Kingdom, respectively. On the other hand, the lowest death/case ratios belong to Russia, Turkey and India, respectively. While, the countries with the highest ratio of COVID-19 recovered/cases are China, Germany and Turkey, the countries with the lowest ratio countries are the UK, USA and France, respectively. Turkey is the most successful country in terms of the recovered and death rates. The lowest performances in these two indicators belong to France and the UK, respectively.

The individual country-level responses are fairly interesting to note:

  • France had issues in getting its health workers into position because a number of them were striking. Despite the fact that France has a good number of hospital beds per capita, this situation combined with PPE shortages led to a somewhat shambolic initial response
  • The United Kingdom had the lowest number of beds and doctors per capita after the United States. It also had (what the authors refer to as) an insufficient number of healthcare workers and PPE kits due to fiscal pressure from the government's austerity policies. In addition, the herd immunity gambit did not work out either
  • Italy and Spain called back retired personnel in order to make up for manpower shortage and to control the alarming level of spread that was seen. However, both countries experienced a medicine and equipment shortage as well. Spain also saw too many frontline workers get infected with COVID-19, causing non-specialists to be switched over to COVID duty without adequate training
  • The United States was not judged in this paper because the health system, by its very nature, is fragmented and thus cannot be judged monolithically. There is a great deal of variance between states in terms of number of doctors, hospital beds, etc. per capita. However, it is notable that the USA continues to face an issue of insifficient numbers of medical staff as well as PPE
  • Turkey and Germany have a fairly good number of doctors and beds per capita. Both countries called up retired doctors and part-time workers respectively to work more and put money into getting COVID treatment to as many people as possible
  • Russia was also seen to have a good number of hospital beds as well as medical personnel per capita. Regardless, Russia pressed medical students into action and provided free COVID care to anyone who required it, building temporary hospitals where needed
  • China was able to impose strict lockdowns and perform extremely effective contact tracing. When this failed, then the building of temporary hospitals and care centres was their way forward
  • Brazil has a state healthcare system (SUS) which covers everyone, with around 80% of the population beholden to the SUS for their healthcare needs. While it does have an advantage in fighting COVID-19 because of its younger population, the prevalence of comorbidities is fairly high

The authors have limited their comments to a small number of countries, but their analysis covers all members of the OECD and a few non-members as well. In order to understand which facets of a health system helped in fighting COVID-19, the authors performed an analysis using artificial neural networks to infer the importance of the following variables:

  1. Number of hospital beds per capita
  2. Number of doctors per capita
  3. Life expectancy at 60
  4. Presence of Universal Health Services
  5. Share of Health Expenditure in GDP

They come to a number of conclusions:

  1. Spending per capita is a bad way of predicting the performance of a health system. The efficiency of a health system (no measure has been given by the authors for calculating this) also makes a difference
  2. The prior institutional experience of a country with infectious diseases seems to be a variable worth considering (not considered in this study)
  3. The speed of the initial response was very important. The faster the initial response, the better the action on the pandemic
  4. The presence or absence of universal healthcare did not matter
  5. The only variable which really mattered was the number of hospital beds per capita

This study is, by necessity, limited. The authors identify a number of variables which can be examined in further studies so as to understand how a health system ought to be designed to prevent a pandemic from overwhelming it.

A review of P4P programmes in LMICs

Singh et. al. perform a realist review of Pay-For-Performance (P4P) programmes in Low and Medium Income Countries (LMICs). They aim to fill the gap left by existing systematic reviews in understanding which parts of a health system contribute to the success or failure of P4P programmes.

P4P means that there is a provision of financial incentives to health professionals contingent on them reaching some types of pre-set performance targets. Notably, in LMICs, P4P programmes also seem to also involve:

  • A shift to e-Health systems
  • New systems of performance and data verification
  • Increased financial decentralisation

Specifically, the authors aimed to answer these questions:

  1. What were the effects of P4P programmes in LMICs?
  2. What contextual factors and mechanisms influence health outcomes when P4P is introduced in health systems in LMICs?
  3. Why and how do these factors and mechanisms influence these outcomes?

It was found that P4P schemes tend to encourage health providers (hospitals, polyclinics, etc.) to invest more time in demand-side strategies to achieve incentivised P4P targets, so they try to offer attractive schemes to draw patients in. In doing so, contrary to what one might expect, providers tend to stick extremely closely to clinical guidelines. This causes quality of care to improve because providers start increasing the time they spend per patient and they also improve the availability of drugs and medical technologies. This has often caused patient trust to rise in these programmes. However, the authors do note that this may be linked to how much money the patient has in his or her pocket.

This also has a significant affect on providers. It tends to increase cash at hand and thus increase financial autonomy, and it also has the possibility of lowering the fees charged for services. Unfortunately, it has also been observed to shift attention from procedures not covered under P4P, causing overall quality of care to diminish.

All this leads to a net rise in patient satisfaction because P4P tends to cause healthcare facilities to invest in infrastructure. This leads to increased availability of services and less friction, which often leads to smaller out of pocket fees (caveat: the authors note that increased quality of services tends to lead to an increased willingness to pay more for them in patients). It also leads to increased provider responsiveness.

P4P programmes have many positive effects on health providers as well. Their productivity was seen to uniformly increase across LMICs. There's also an increase in standards of governance and accountability, espcially where administrative roles are well-defined. A major part of this has to do with timely payments, which have a positive association with increased productivity. In such cases, healthcare providers have to depend upon the efficiency of the banking system in their area, procedural efectiveness of their own administration, and the speed with which the donor (the one paying for the P4P programme) disburses money.

Unfortunately, this does not necessarily lead to increased health provider motivation. The authors state that fiscal and administrative autonomy combined with the introduction of P4P programmes themselves may contribute to increased motivation. The extra income from P4P is also seen as a net positive. However, many health providers might not get to keep this money if they are not given financial autonomy and so may not have any motivation to improve. The more decentralised the health system, the greater the positive effect on health providers.

There is a negative side to P4P programmes as well. They tend to encourage providers to game the system and misreport data in order to make more money, especially in countries with inadequate verification mechanisms and low-quality checks and balances. They can also lead to a net increase in prices of services if price are already at rock bottom. And finally, P4P programmes tend to work well in countries with good existing health systems, and ought to be seen as part of a multi-pronged strategy to improve systemic capability.

Understanding the use of patient reported data by insurance agencies

Neubert et. al. deal with a very important topic for our times. They review various studies which talk about the kinds of self-reported health data collected by insurers on patients and how it is utilised. In addition, they also try to see what the motivations of actually collecting these data are so as to understand how this paradigm fits into an insurance agency's wider goals. In order to do so, the authors talk about two very important concepts. Quality of care (QoC), and value-based healthcare. In other words, they may be thought of as variables which look at patient satisfaction/outcomes and bang for buck respectively.

The authors find that insurers look for two types of indicators in general:

  • PROMS - Patient Reported Outcome Measures, which tend to measure how patients view the progress of their medical trysts ("I feel better after having followed the doctor's advice, but I think I still need another session," or "I'd rate my pain a 4/10, down from a 6/10 a week ago.")
  • PREMS - Patient Reported Experience Measures ("I liked the way the hospital handled MRI scanning," or "I loved the fact that they have experts from three different fields handling prenatal counseling.")

There are other indicators which insurance agencies take into account as well, such as structural indicators (the presence of specific technologies and programmes such as an MRI machine or an HIV/AIDS programme), process indicators (how long did it take to get a certain type of job done and whether it as efficient) and clinical outcome indicators (did the patient survive, have they been put on drugs to manage something chronically). Structural indicators tend to be the least used and generally tend to be reported along with process and clinical outcome indicators, both of which were much more common.

Insurers tend to use this data in multiple ways. PREMS and PROMS are used a lot when deciding to purchase certain services from specific service providers. QoC depends on data availability so it might not be used to make decisions here even though it might be a preferred metric. Value-based healthcare is generally assessed using cost and volume data, especially when going for a selective contracting agreement.

A selective contracting arrangement (SCA) is an arrangement for the payment of predetermined fees or reimbursement levels for covered services by the carrier to preferred providers or preferred provider organizations (PPOs)

The authors also note that different PREMs and PROMs are used for selective contracting and P4P programmes. For the first, all you wish to do is to compare providers with each other. However, for the seocnd you wish to compare providers to an objective benchmark or standard.

Insurance agencies use this data for the purposes of quality assurance and quality improvement. Three perspectives emerge about QoC measures in particular:

  • They're used as an ancillary instrument to inform decisions of insurers
  • They're used as a means of supporting and enhancing quality improvement via benchmarking providers
  • PREMs tend to be used for this task more than other indicators

QoC tends to be a major part of healthcare, and so insurers spend a lot of time on it. They look at measures like effectiveness, efficiency, access, patient-centredness, equity, and safety.

On the other hand, PROMs and claims data are used to get information about populations at risk. All this data is then used to develop new products and programmes.

The authors make several interesting comments which I wish to highlight:

  • PROMs tend to be the most popular way of collecting patient-reported health data, followed by PREMs. Structural, process, and clinical outcome indicators trail these two
  • The breadth to which this data is used varies wildly across insurers. Insurers need to have a robust system to collect this data, the culture of the insurer ought not to be paternalistic ("I know what's best for patients, there's no point in asking them"), the laws of the jurisdiction needs to allow for collection of needed data, and market forces need to exert pressure to improve services
  • PROMs and PREMs are not a stable set of indicators, new measures are continuously being added to these and old measures being removed
  • Health insurers are starting to become drivers of provider performance
  • There is a great deal of heterogeneity in the terms used in the literature regarding these measures

The authors also report the following limitations which health insurers grapple with:

However, our study highlights three key aspects that hinder a more robust use of such data in a health insurer’s business. First, the insurers’ use of patient-reported data is affected by a large technological and methodological heterogeneity that inhibits the transferability of innovative and effective initiatives across contexts. Second, the varying terminology of constructs used by the many stakeholders with whom an insurer interacts. Third, the involvement of insured people by insurers in the development of patient-reported measures and decision-making in regard to a health insurer’s strategy and practices is still limited. To overcome these hindering factors, health insurers are advised to be more explicit in regard to the role they want to play within the health system and society at large. In addition, health insurers should have a clear scope about the use and actionability of patient-reported measures, and further involve insurees to the extent where it is feasible and deemed necessary.

Comprehensive profiling for non-small-cell lung cancer: health and budget impact

Johnston et. al. write about the health and budget impacts of genomic testing for detecting mutations pertinent to non-small-cell lung cancer. This study was funded by a company which aims to sell a solution for this type of genomic profiling. Specifically, the authors focus on the Foundation One CDx and Liquid tests, which are assays used to profile multiple known genomic mutations in a single assay. CDx uses a small amount of tissue to perform the assay and Liquid requires a blood sample.

The authors created a model using the societal perspective in Ontario, Canada, and took workplace productivity into account for this study. They saw that the use of Foundation Medicine Tests was associated with an increase in budget impact as well as increased life years and workplace productivity. The authors note that multiple gene testing generally leads to a decrease in turnaround time and improved detection rates, but the advantage of using Foundation Medicine products was in smaller amounts of initial tissue sample required. Their study also showed an increase in the use of targeted therapies when using Foundation Medicine products, which they hypothesize has the potential to increase quality of life for patients as compared to traditional broad-spectrum chemotherapy.

Unfortunately, I would take this study with a grain of salt because of the involvement of Foundation Medicine itself in these studies. Certain assumptions were based on clinical experience, and while I don't think there was any reason to suspect academic dishonesty in this paper, I would still wait for independent corroboration of these results.

Food consumption patterns and practices during COVID-19

Murphy et. al. examine changing food habits during the COVID-19 pandemic and the effect it had on the quality of diets and the food system. Their hope is that this information may be used to increase food supply chain resilience. They base their study upon observations in selected cohorts in Ireland, Great Britain, New Zealand, and the United States of America. Their results are fairly straightforward:

  • Ireland and Great Britain saw increases in the use of fresh ingredients for preparing meals
  • Ireland, Great Britain and New Zealand saw a reduction in food waste, a reduction in takeout and ready-made food, and an increase in baking
  • There were reports of there being increased difficulty in all countries in finding ingredients as well as in bulk buying
  • The authors report that people in all countries began to increasingly plan their food shopping by making lists, for example
  • Great Britain saw an increase in fruit-eating, while Ireland, New Zealand and Great Britain all saw an increase in vegetable eating
  • The consumption of saturated fats also went up in all three countries

Ireland saw the greatest change towards eating vegetables, the use of fresh ingredients, reducing food waste, and other positive indicators mentioned in this paper. The authors note that the Irish cohort which was used for this study was predominantly comprised of young, highly-educated females, and so results may have been skewed in its favour. Ireland and New Zealand also reported the lowest difficulty in finding and sourcing ingredients. It is also interesting to see how the United States is notably absent when talking about positive food trends, but it too suffered from similar food shortages as suffered in other countries.

The main takeaway from this paper was that countries which had more targeted and focused restrictions in place were better at managing their food supply chains.

The relationship between formal credit constraints and health status in China

Yang et. al. use the Chinese Household Income Project (CHIP) data to link rural Chinese households' self-reported health status with the formal credit constraints they face from financial institutions. Typically, research in this area focuses on individual demographic attributes, social attributes, the behaviour of individuals, and the impact of environmental quality and hazards. However, this study looks at a different link. In the authors' own words:

Our main contributions are as follows: first, by examining the impact of formal credit constraints on rural residents’ health, we contribute to two strands of literature: credit constraints and individual health. Second, this study clarifies the mechanism of credit constraints on rural residents’ health theoretically and empirically. Third, this study provides a decision-making reference for the government to improve rural residents’ health policy from credit constraints and credit supply improvement. This study is the first attempt to empirically analyze the impact of credit constraints on rural residents’ health in China to the best of our knowledge.

The authors used a theoretical framework derived with the help of two perspectives: credit demand perspective, and credit supply perspective. The credit demand perspective relies upon the theory of credit demand repression. Demand repression tends to happen, according to the authors, when financial institutions set high transaction costs and stringent credit conditions for disbursing loans. This leads to an increase in loan rejection rates. The credit supply perspective deals with the credit rationing theory. As the authors explain, under conditions of fixed interest rates, facing adverse selection and information asymmetry, banks are forced to adopt conditions like handling fees etc. which price consumers out of the market.

These constraints then tend to disrupt income and consumption while increasing economic vulnerability. This can take many forms, for example, lack of credit might cause someone to forgo higher education because there is no way of paying the requisite fees. This hinders promotion of human capital. It also reduces the ability to plan across long time-scales. This has, among other things, a negative impact on entrepreneurship.

In order to understand this relationship empirically, the authors utilised 3 variables: self-related health, days in which illness was reported, and formal credit constraints. These variables were controlled with respect to age, gender, ethnicity, etc. The authors found that 27.02% of rural residents in their sample were found to have credit constraints. In their own words:

Formal credit constraints reduce the probability that a rural resident judges their health condition as good and increases the probability that a rural resident judges their health condition as bad.

An ordered probit model showed the causality beween credit constraints and self-reported health, and a tobit model showed the causality between credit constraints and the number of days taken off from work due to illness.

The authors conclude by saying that these results show that it is a good idea to reduce credit constraints, push people towards formal lending mechanisms through trust-building programmes, and to train people to choose profitable enterprises to sink their money in.

However, the study misses out on a major pillar: the presence of informal credit sources and informal credit constraints (this is a gap which the authors are aware of). A future study ought to also characterise informal credit sources and constraints and see whether their relationship with health status is any different.

The role of governments in information dissemination

Lu et. al. examine the role of governments in information dissemination in the context of COVID-19. The authors state that information travels faster than the virus, and so timely dissemination of information is extremely important. However, disseminating false information, either deliberately or mistakenly, can lead to major issues. Misinformation tends to have a natural sort of resonance with public opinions: people often get illogical or fail to understand the different between true and false statements.

The government has a central role in blocking and spreading information. Minimal blocking of information leads to an open information network but it can lead to uncontrolled spread of rumours. This has the potential to prevent pandemic prevention efforts. To quote the paper:

It is always believed that government should perform as a central node to disclose accurate and up-to-date information to the entire society, so as to keep the public away from untruthful information and prompt the public to make informed decisions about health protection. However, in the real world, governments do face time constraints and the trade-off between being accurate and being up-to-date in terms of information disclosing, which is not considered in classical information theory. Because a highly infectious disease caused by unknown viruses with great externality, such as COVID-19, spreads together with information of varying qualities (truthfulness, accuracy, etc.), it is highly probable that the disease has already contaminated the society before low-quality information is purged. In this case, the government has no way to disclose accurate information in time, resulting in the loss of public trust and raising the doubt of the public on the governing capacity of the government, which will accelerate epidemic outbreak. Therefore, governments need to not only decide when to inject information into the network, but also whether to follow the tenet that governments do not and should not block information spreading at any circumstance.

The major conclusion in literature on this topic has been that governments ought not to interfere with the spread of information in any way in order to maximise welfare. Unfortunately this requires two assumptions to be true. One is that the publishers of this information are perfectly competitive (a perfectly open and fair marketplace) and two, that there is no time constraint. These assumptions do not really hold in the real world. The authors' conclusions are somewhat illuminating:

We introduce the non-dualism of information and the heterogeneity of nodes’ behaviors into the epidemic model and conduct a simulation to reveal the information intervention dilemma faced by the government between information disclosing and blocking. We find that governments face a trade-off between speed and accuracy in information disclosing; and the optimal strategy is contingent on varying conditions in information blocking. The optimal combination of disclosing and blocking is highly sensitive to the government preference and its governance capacity. Governments that are only responsible for the outcome of intervention will focus unilaterally on the accuracy at the expense of speed; a risk-averse government that intends to minimize the maximum infection rate under uncertain scenarios will impose a more restrictive blocking; and the most restrictive blocking strategy might be the best for governments with lower capability and credibility.

It truly makes one wonder where one's own government stands.

The effect of lottery wins on health

Kim and Koh examine the effect of exogenous deluges of money on self-reported health statuses. In order to do so, they focus on lottery wins in Singapore. Their results show that a win of S$10,000 led to a measurable increase in self-reported health status. It does not affect the amount of money spent on healthcare, cigarettes, etc. at all. It also does not discourage the lottery winner from going to work.

The authors conclude that the psychological effects of the lottery win and the general increase in household spending (the latter of which can be measured) may account for the increase in health status of a an individual.