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.

Brendan Collins’s journal round-up for 14th January 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.

Income distribution and health: can polarization explain health outcomes better than inequality? The European Journal of Health Economics [PubMed] Published 4th December 2018

One of my main interests is health inequalities. I thought polarisation was intuitive; I had seen it in the context of the UK and the US employment market; an increase in poorly-paid ‘McJobs’ and an increase in well-paid ‘MacJobs’, with fewer jobs in the middle. But I hadn’t seen polarisation measured in a statistical way.

Traditional measures of population inequalities like Gini or Atkinson index measure the share of income or the ratio of richest to poorest. But polarisation goes a step further and looks whether there are discrete clusters or groups who have similar incomes. The theory goes that having discrete groups increases social alienation, conflict and socioeconomic comparison and increases health inequalities. Now, I get how you can test statistically for discrete income clusters, and there is an evidence base for the relationship between polarisation and social tension. But groups will cluster based on other factors besides income. I feel like it may be taking a leap to assume a statistical finding (income polarisation) will always represent a sociological construct (alienation) but I confess I don’t know the literature behind this.

China is a country with an increasing degree of polarisation as measured by the Duclos, Esteban and Ray (DER) polarisation indices, and this study suggests that it is related to health status. This study looked at trends in BMI and systolic blood pressure from 1991 to 2011 and found both to increase with increased polarisation. I imagine a lot of other social change went on in this time period in China. I think BMI might not be a good candidate for measuring the effect of polarisation, as being poor is associated with malnourishment and low weight as well as obesity. The authors found that social capital (based on increasing family size, community size, and living in the same community for a long time) had a protective effect against the effects of polarisation on health. Whether this study provides more evidence for the socioeconomic comparison or status anxiety theories of health inequalities, I am not sure; it could equally provide evidence for the neo-materialist (i.e. simply not having enough resources for a healthy life) theories – the relative importance will likely differ by country anyway.

Maybe we don’t need to add more measures of inequality to the mix but I am intrigued. I am just starting my journey with polarisation but I think it has promise.

Two-year evaluation of mandatory bundled payments for joint replacement. The New England Journal of Medicine [PubMed] Published 2nd January 2019

Joint replacements are a big cost to western healthcare systems and often delayed or rationed (partly because replacement joints may only have a 10-20 year lifespan on average). In the UK, for instance, joint replacements have been rationed based on factors like BMI or pain levels (in my opinion, often in an arbitrary way to save money).

This paper found that having a bundled payments and penalties model (Comprehensive Care for Joint Replacement; CJR) for optimal care around hip and knee replacements reduced Medicare spending per episode compared to areas that did not pilot the programme. The overall difference was small in absolute terms at $812 against a total cost of around $24,000 per episode. The programme involves the hospital meeting a set of performance measures, and if they can do so at a lower cost, any savings are shared between the hospital and the payer. Cost savings were mainly driven by a reduction in patients being discharged to post-acute care facilities. Rates of complex patients were similar between pilot and control areas – this is important because a lower rate of complex cases in the CJR trial areas might indicate hospitals ‘cherry picking’ easier to treat, less expensive cases. Also, rates of complications were not significantly different between the CJR pilot areas and controls.
This paper suggests that having this kind of bundled payment programme can save money while maintaining quality.

Association of the Hospital Readmissions Reduction Program with mortality among Medicare beneficiaries hospitalized for heart failure, acute myocardial infarction, and pneumonia. JAMA [PubMed] Published 25th December 2018

Nobody likes being in hospital. But sometimes hospitals are the best places for people. This paper looks at possible unintended consequences of a US programme; the Hospital Readmissions Reduction Program (HRRP) where the Centers for Medicare & Medicaid Services (CMS) impose financial penalties (almost $2billion dollars’ worth since 2012) on hospitals with elevated 30-day readmission rates for patients with heart failure, acute myocardial infarction, and pneumonia. This study compared four time periods (no control group) and found that, after the programme was implemented, death rates for people who had been admitted with pneumonia and heart failure increased, with these increased deaths occurring more in people who had not been readmitted to hospital. The analysis controlled for differences in demographics, comorbidities, and calendar month using propensity scores and inverse probability weighting.

The authors are clear that their results do not establish cause and effect but are concerning nonetheless and worthy of more analysis. Incidentally, there is another paper this week in Health Affairs which suggests that the benefits of the programme in reducing readmissions was overstated.

There has been a similar financial incentive in the English NHS where hospitals are subject to the 30-day readmission rule, meaning they are not paid for people who are readmitted as an emergency within 30 days of being discharged. This is shortly to be abolished for 2019/20. I wonder if there has been similar research on whether this also led to unintended consequences in the NHS. Maybe there is a general lesson here about thinking a bit deeper about the potential outcomes of incentives in healthcare markets?

In these last two papers, we have had two examples of financial incentive programmes from Medicare. The CJR, which seems to have worked, has been dampened down from a mandatory to a voluntary programme, while the HRRP, which may not have worked, has been extended.

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Chris Sampson’s journal round-up for 31st December 2018

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.

Perspectives of patients with cancer on the quality-adjusted life year as a measure of value in healthcare. Value in Health Published 29th December 2018

Patients should have the opportunity to understand how decisions are made about which treatments they are and are not allowed to use, given their coverage. This study reports on a survey of cancer patients and survivors, with the aim of identifying patients’ awareness, understanding, and opinions about the QALY as a measure of value.

Participants were recruited from a (presumably US-based) patient advocacy group and 774 mostly well-educated, mostly white, mostly women responded. The online survey asked about cancer status and included a couple of measures of health literacy. Fewer than 7% of participants had ever heard of the QALY – more likely for those with greater health literacy. The survey explained the QALY to the participants and then asked if the concept of the QALY makes sense. Around half said it did and 24% thought that it was a good way to measure value in health care. The researchers report a variety of ‘significant’ differences in tendencies to understand or support the use of QALYs, but I’m not convinced that they’re meaningful because the differences aren’t big and the samples are relatively small.

At the end of the survey, respondents were asked to provide opinions on QALYs and value in health care. 165 people provided responses and these were coded and analysed qualitatively. The researchers identified three themes from this one free-text question: i) measuring value, ii) opinions on QALY, and iii) value in health care and decision making. I’m not sure that they’re meaningful themes that help us to understand patients’ views on QALYs. A significant proportion of respondents rejected the idea of using numbers to quantify value in health care. On the other hand, some suggested that the QALY could be a useful decision aid for patients. There was opposition to ‘external decision makers’ having any involvement in health care decision making. Unless you’re paying for all of your care out of pocket, that’s tough luck. But the most obvious finding from the qualitative analysis is that respondents didn’t understand what QALYs were for. That’s partly because health economists in general need to be better at communicating concepts like the QALY. But I think it’s also in large part because the authors failed to provide a clear explanation. They didn’t even use my lovely Wikipedia graphic. Many of the points made by respondents are entirely irrelevant to the appropriateness of QALYs as they’re used (or in the case of the US, aren’t yet used) in practice. For example, several discussed the use of QALYs in clinical decision making. Patients think that they should maintain autonomy, which is fair enough but has nothing to do with how QALYs are used to assess health technologies.

QALYs are built on the idea of trade-offs. They measure the trade-off between life extension and life improvement. They are used to guide trade-offs between different treatments for different people. But the researchers didn’t explain how or why QALYs are used to make trade-offs, so the elicited views aren’t well-informed.

Measuring multivariate risk preferences in the health domain. Journal of Health Economics Published 27th December 2018

Health preferences research is now a substantial field in itself. But there’s still a lot of work left to be done on understanding risk preferences with respect to health. Gradually, we’re coming round to the idea that people tend to be risk-averse. But risk preferences aren’t (necessarily) so simple. Recent research has proposed that ‘higher order’ preferences such as prudence and temperance play a role. A person exhibiting univariate prudence for longevity would be better able to cope with risk if they are going to live longer. Univariate temperance is characterised by a preference for prospects that disaggregate risk across different possible outcomes. Risk preferences can also be multivariate – across health and wealth, for example – determining the relationship between univariate risk preferences and other attributes. These include correlation aversion, cross-prudence, and cross-temperance. Many articles from the Arthur Attema camp demand a great deal of background knowledge. This paper isn’t an exception, but it does provide a very clear and intuitive description of the various kinds of uni- and multivariate risk preferences that the researchers are considering.

For this study, an experiment was conducted with 98 people, who were asked to make 69 choices, corresponding to 3 choices about each risk preference trait being tested, for both gains and losses. Participants were told that they had €240,000 in wealth and 40 years of life to play with. The number of times that an individual made choices in line with a particular trait was used as an indicator of their strength of preference.

For gains, risk aversion was common for both wealth and longevity, and prudence was a common trait. There was no clear tendency towards temperance. For losses, risk aversion and prudence tended to neutrality. For multivariate risk preferences, a majority of people were correlation averse for gains and correlation seeking for losses. For gains, 76% of choices were compatible with correlation aversion, suggesting that people prefer to disaggregate fixed wealth and health gains. For losses, the opposite was true in 68% of choices. There was evidence for cross-prudence in wealth gains but not longevity gains, suggesting that people prefer health risk if they have higher wealth. For losses, the researchers observed cross-prudence and cross-temperance neutrality. The authors go on to explore associations between different traits.

A key contribution is in understanding how risk preferences differ in the health domain as compared with the monetary domain (which is what most economists study). Conveniently, there are a lot of similarities between risk preferences in the two domains, suggesting that health economists can learn from the wider economics literature. Risk aversion and prudence seem to apply to longevity as well as monetary gains, with a shift to neutrality in losses. The potential implications of these findings are far-reaching, but this is just a small experimental study. More research needed (and anticipated).

Prospective payment systems and discretionary coding—evidence from English mental health providers. Health Economics [PubMed] Published 27th December 2018

If you’ve conducted an economic evaluation in the context of mental health care in England, you’ll have come across mental health care clusters. Patients undergoing mental health care are allocated to one of 20 clusters, classed as either ‘psychotic’, ‘non-psychotic’, or ‘organic’, which forms the basis of an episodic payment model. In 2013/14, these episodes were associated with an average cost of between £975 and £9,354 per day. Doctors determine the clusters and the clusters determine reimbursement. Perverse incentives abound. Or do they?

This study builds on the fact that patients are allocated by clinical teams with guidance from the algorithm-based Mental Health Clustering Tool (MHCT). Clinical teams might exhibit upcoding, whereby patients are allocated to clusters that attract a higher price than that recommended by the MHCT. Data were analysed for 148,471 patients from the Mental Health Services Data Set for 2011-2015. For each patient, their allocated cluster is known, along with a variety of socioeconomic indicators and the HoNoS and SARN instruments, which go into the MHCT algorithm. Mixed-effects logistic regression was used to look at whether individual patients were or were not allocated to the cluster recommended as ‘best fit’ by the MHCT, controlling for patient and provider characteristics. Further to this, multilevel multinomial logit models were used to categorise decisions that don’t match the MHCT as either under- or overcoding.

Average agreement across clusters between the MHCT and clinicians was 36%. In most cases, patients were allocated to a cluster either one step higher or one step lower in terms of the level of need, and there isn’t an obvious tendency to overcode. The authors are able to identify a few ways in which observable provider and patient characteristics influence the tendency to under- or over-cluster patients. For example, providers with higher activity are less likely to deviate from the MHCT best fit recommendation. However, the dominant finding – identified by using median odds ratios for the probability of a mismatch between two random providers – seems to be that unobserved heterogeneity determines variation in behaviour.

The study provides clues about the ways in which providers could manipulate coding to their advantage and identifies the need for further data collection for a proper assessment. But reimbursement wasn’t linked to clustering during the time period of the study, so it remains to be seen how clinicians actually respond to these potentially perverse incentives.

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