Jason Shafrin’s journal round-up for 9th September 2019

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Price effects of a hospital merger: heterogeneity across health insurers, hospital products, and hospital locations. Health Economics [PubMed] [RePEc] Published 1st July 2019

Most economics literature indicates that hospital mergers typically result in higher prices. But what does higher prices mean? Does it mean higher prices for all services? Higher prices for all health insurers?

Many economic models assume that hospitals charge a standard base rate and charges for individuals’ procedures are a fixed ratio of the base across all hospitals. This approach would make sense in a DRG-based system where prices are proportional to the product of a hospital’s base rate and the Medicare Severity DRG specific weight for a given hospitalization.

In practice, however, it is possible for prices to vary across procedures, across different negotiated contracts with insurers, and even across different locations within the same hospital system. For instance, the economic theory in this paper shows that the effect of a hospital merger increases prices most when an insurer’s bargaining power is high. Why? Because if the insurer had weak bargaining power, the hospital already would have high prices; the marginal impact is only felt when insurers had market power to begin with. Another interesting theoretical prediction is that if substitution between hospitals is stronger for service A than service B, prices will increase more for the former product, since the merger decreases the ability of consumers to substitute across hospitals due to decreased supply.

In their empirical applications, the authors use a comprehensive nationwide patient‐level data set from the Netherlands, on hospital admissions and prices. The study looks at three separate services: hip replacement, knee replacement, and cataract surgery. They use a difference-in-difference approach to measure the impact of a merger on prices for different services and across payers.

Although the authors did replicate earlier findings and showed that prices generally rise after a merger, the authors found significant heterogeneity. For instance, prices rose for hip replacements but not for knee replacements or cataracts. Prices rose for four health insurers but not for a fifth. In short, while previous findings about average prices still hold, in the real world, the price impact is much more heterogeneous than previous models would predict.

The challenges of universal health insurance in developing countries: evidence from a large-scale randomized experiment in Indonesia. NBER Working Paper [RePEc] Published August 2019

In 2014, the Indonesian government launched Jaminan Kesehatan Nasional (JKN), a national, contributory health insurance program that aimed to provide universal health coverage by 2019. The program requires individuals to pay premiums for coverage but there is an insurance mandate. JKN, however, faced two key challenges: low enrollment and high cost. Only 20% of eligible individuals enrolled. Further, the claims paid exceeded premiums received by a factor of more than 6 to 1.

This working paper by Banerjee et al describes a large-scale, multi-arm experiment to examine three interventions to potentially address these issues. The interventions included: (i) premium subsidy, (ii) transaction cost reduction, and (iii) information dissemination. For the first intervention, individuals received either 50% or 100% premium subsidy if they signed up within a limited time frame. For the second intervention, households received at-home assistance to enroll in plans through the online registration system (rather than traveling to a distant insurance office to enroll). For the third intervention, the authors randomized some individuals to receive various informational items. The real benefit of this study is that people were randomized to these different interventions.

Using this study design, the authors found that premium assistance did increase enrollment. Further, premium assistance did not affect per person costs since the individuals who enrolled were healthier on average. Thus, the fear that subsidies would increase adverse selection was unfounded. The authors also found that offering help in registering for insurance increased enrollment. Thus, it appears that the ‘hassle cost’ of signing up for a government program represents a real hassle with tangible implications. However, the additional insurance information provided had no effect on enrollment.

These results are both encouraging and discouraging. Premium subsidies work and do not drive up cost per person. However, enrollment levels – even with a 100% premium subsidy and assistance registering for insurance – were only at 30%. This figure is far better than the baseline figure of 8%, but far from the ‘universal’ coverage envisioned by the creators of JKN.

Credits

Thesis Thursday: Firdaus Hafidz

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Firdaus Hafidz who has a PhD from the University of Leeds. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Assessing the efficiency of health facilities in Indonesia
Supervisors
Tim Ensor, Sand Tubeuf
Repository link
http://etheses.whiterose.ac.uk/id/eprint/21575

What are some of the key features of health and health care in Indonesia?

Indonesia is a diverse country, with more than 17 thousand islands and 500 districts. Thus, there is a wide discrepancy of health outcomes across Indonesia, which also reflects the country’s double burden of both communicable and emerging non-communicable diseases. Communicable diseases such as tuberculosis, diarrhoea and lower respiratory tract infections remain as significant issues in Indonesia, especially in remote areas. At the same time, non-communicable diseases are becoming a major public health problem, especially in urban areas.

Total healthcare expenditure per capita grew rapidly, but in certain outcomes, such as maternal mortality rate, Indonesia performs less well than other low- and middle-income countries. Health facilities represent the largest share of healthcare expenditures, but utilisation is still considered low in both hospitals and primary healthcare facilities. Given the scarcity of public healthcare resources, out-of-pocket expenditure remains considerably higher than the global average.

To reduce financial barriers, the Government of Indonesia introduced health insurance in 1968. Between 2011 and 2014, there were three major insurance schemes: 1) Jamkesmas – poor scheme; 2) Jamsostek – formal sector workers scheme; and 3) Askes – civil servant scheme. In 2014, the three schemes were combined into a single-entity National Health Insurance scheme.

What methods can be used to measure the efficiency of health care in low and middle-income countries?

We reviewed measurements of efficiency in empirical analyses conducted in low- and middle-income countries. Methods, including techniques, variables, and efficiency indicators were summarised. There was no consensus on the most appropriate technique to measure efficiency, though most existing studies have relied on ratio analysis and data envelopment analysis because it is simple, easy to compute, low-cost and can be performed on small samples. The physical inputs included the type of capital (e.g. the number of beds and size of health facilities) and the type of labour (e.g. the number of medical and non-medical staff). Most of the published literature used health services as outputs (e.g. the number of outpatient visits, admission and inpatient days). However, because of poor data availability, fewer studies used case-mix and quality indicators to adjust outputs. So most of the studies in the literature review assumed that there was no difference in the severity and effectiveness of healthcare services. Despite the complexity of the techniques, researchers are responsible to provide interpretable results to the policymakers to guide their decisions for a better health policy on efficiency. Adopting appropriate methods that have been used globally would be beneficial to benchmark empirical studies.

Were you able to identify important sources of inefficiency in Indonesia?

We used several measurement techniques including frontier analysis and ratio analysis. We explored contextual variables to assess factors determining efficiency. The range of potential models produced help policymakers in the decision-making process according to their priority and allow some control over the contextual variables. The results revealed that the efficiency of primary care facilities can be explained by population health insurance coverage, especially through the insurance scheme for the poor. Geographical factors, such as the main islands (Java or Bali), better access to health facility, and location in an urban area also have a strong impact on efficiency. At the hospitals, the results highlighted higher efficiency levels in larger hospitals; they were more likely to present in deprived areas with low levels of education; and they were located on Java or Bali. Greater health insurance coverage also had a positive and significant influence on efficiency.

How could policymakers improve the efficiency of health care in Indonesia or other similar settings?

I think there are several ideas. First, we need to have a careful tariff adjustment as we found an association between low unit costs and high efficiency scores. Case base group tariffs need to account for efficiency scores to prevent unnecessary incentives for the providers, exacerbating inefficiency in the health system.

Secondly, we need flexibility in employment contracts, particularly for the less productive civil servant worker so the less productive worker could be reallocated. We also need a better remuneration policy to attract skilled labour and improve health facilities efficiency.

From the demand side, reducing physical barriers by improving infrastructure could increase efficiency in the rural health care facilities through higher utilisation of care. Facilities with very low utilisation rates still incur a fixed cost and thus create inefficiency. Through the same argument we also need to reduce financial barriers using incentives programmes and health insurance, thus patients who are economically disadvantaged can access healthcare services.

How would you like to see other researchers build on your work?

Data quality is crucial in secondary data analysis research, and it was quite a challenge in an Indonesian setting. Meticulous data management is needed to mitigate data errors such as inconsistency, outliers and missing values.

As this study used a 2011 cross-sectional dataset, replicating this study using a more recent and even longitudinal data would highlight changes in efficiency due to policy changes or interventions. Particularly interesting is the effect of the 2014 implementation of Indonesian national health insurance.

My study has some limitations and thus warrants further investigation. The stochastic frontier analysis failed to identify any inefficiency at hospitals when outpatient visits were included. The statistical errors of the frontier function cannot be distinguished from the inefficiency effect of the model. It might be related to the volume and heterogeneity of outpatient services which swamps the total volume of services and masks any inefficiency.

Thesis Thursday: Angela Devine

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Angela Devine who has a PhD from The Open University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
The economics of vivax malaria treatment
Supervisors
Yoel Lubell, Ric Price, Ricardo Aguas, Shunmay Yeung
Repository link
https://thesiscommons.org/zsc6x/

What is vivax malaria and what are some of the key challenges that it presents for health economists?

One infectious bite from a mosquito carrying vivax malaria can lead to multiple episodes of malaria due to dormant liver parasites called hypnozoites. We can’t tell the difference between these relapse infections and new infections, which means that it’s challenging to model. Unlike falciparum malaria, which frequently results in severe outcomes and deaths, vivax malaria doesn’t often result in direct mortality. Instead, it likely causes indirect mortality through the malnutrition and anaemia that are caused by repeated malaria episodes. Unfortunately, the evidence of this is limited.

To prevent future relapses, patients need to be given a drug to treat the liver parasites (radical cure) in addition to treating the blood stage treatment. The only drug that is currently licensed for radical cure, primaquine, can cause potentially life-threatening haemolysis in individuals who have a genetic disorder called glucose-6-phosphate-dehydrogenase (G6PD) deficiency. While some countries are so concerned about haemolytic events that primaquine isn’t used at all, other settings prescribe primaquine to everyone. The evidence on the risk of primaquine-induced haemolysis and death is sparse, and expert opinion on this matter is fiercely divided.

How did you go about collecting the data needed for your study?

Not much has been done previously on vivax malaria costs, which meant that a lot of my work involved generating cost data. I started by analysing some fairly old data that my supervisors had from a study on treatment-seeking behaviour in Papua, Indonesia. The cost of illness study indicated that household costs were similar for both vivax and falciparum malaria in 2006. I also collected provider and patient-level cost data alongside a multi-country clinical trial on vivax malaria treatment. I wasn’t able to travel to the some of the study sites (e.g. Afghanistan) to collect the provider costs, so I had to create worksheets for local trial staff to fill out. It was an iterative process, particularly with the first site in Indonesia, but it got faster and easier to do each time. I’m very thankful that the local teams were enthusiastic about this work and patient with my many questions and requests.

What is the economic burden of vivax malaria and who bears the cost?

A lot of vivax malaria episodes occur in remote areas where access to care is limited. The highest incidence of the disease is in children, particularly those under the age of five. This often means that someone will need to take time off from usual activities, such as farming, attending school, or household chores, to care for the sick. I estimated the global economic burden to be US$330 million. These estimates don’t include mortality, malnutrition or anaemia. Since we know that repeated episodes can have a profound impact on a household’s income, I included productivity losses for those who were ill and their carers. We also know that malaria causes educational losses, so I included these productivity costs for children as well as adults to try to capture some of those losses. In total, productivity losses accounted for US$263 million, nearly 80% of the total costs. Since many who are affected by this disease aren’t paid for their work, I used one GDP per capita per day for every day lost to illness or caretaking. Other methods of valuing these losses would have a substantial impact on the total costs. While there’s a considerable amount of uncertainty around some of the numbers I used and assumptions that I made, my hope is that by identifying the issues, we will be able to generate the data needed for better estimates in the future.

What methods did you use to evaluate the cost-effectiveness of new treatment strategies?

Asia-Pacific malaria control programs stated that the cost of G6PD screening was an obstacle to its widespread use. My research addressed those concerns through a decision tree model in R that weighed up the costs, risks and benefits of screening using newly developed G6PD rapid diagnostic tests (RDTs) before prescribing primaquine. I wanted to make this work as relevant to policymakers as possible, so I did two separate comparisons. First I compared this strategy to not using primaquine, then I compared it to prescribing primaquine to everyone without screening. While this strayed from typical economic evaluation methods, it seemed unlikely that a setting where primaquine isn’t prescribed due to fear of haemolysis would switch to prescribing primaquine to everyone without screening, or that a setting where primaquine is prescribed to everyone would stop using it altogether.

As G6PD deficiency is X-linked, the risk of haemolysis varies by gender, so results need to be stratified by gender. The prevalence and severity of G6PD deficiency and the latency period and number of relapses for vivax malaria varies geographically. While I wanted to have more than one setting to explore how these might impact the results, four comparisons was already a lot of information to present. Instead, I used R-shiny with my model to create an interactive website where people can see how changes in the baseline model parameters impact the results. My goal was to provide a tool that policymakers could use to help make decisions about treatment strategies in their settings. This also provides an opportunity to explore the impact of parameter values that may be seen as contentious.

What are some of the issues you encountered in working with policymakers to ensure that cost-effective treatments become more widely used?

One issue is that patients, especially those who can afford to do so, seek treatment in the private sector, which is harder to control. Encouragingly, the follow-up survey in Papua, Indonesia indicated that changing treatment policy in the public sector also had an impact on how private sector providers diagnosed and treated malaria. As someone keen to influence policy, I benefited a lot from meetings with malaria control program officials from the Asia Pacific. These provided insights on the challenges that countries are facing. For example, the work I did on G6PD screening was aimed at addressing the cost issue that kept coming up in these meetings. Unfortunately, I’m not aware of settings where they have begun to routinely use G6PD RDTs. There are additional barriers, like getting the tests licensed so that malaria control programs can purchase them with a subsidy from The Global Fund. Another issue that I hadn’t fully appreciated before beginning my PhD is that funding for diseases like malaria is often siloed for specific purposes by the various donors. This can make it more challenging to ensure that countries are getting the best possible value for the money that is spent. There’s also been a lot of debate recently about what willingness to pay threshold should be used in poorly resourced settings. This is a debate that we need to have, but it also makes it more challenging to decide which treatments should be considered to be cost-effective.