Chris Sampson’s journal round-up for 18th February 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.

An educational review about using cost data for the purpose of cost-effectiveness analysis. PharmacoEconomics [PubMed] Published 12th February 2019

Costing can seem like a cinderella method in the health economist’s toolkit. If you’re working on an economic evaluation, estimating resource use and costs can be tedious. That is perhaps why costing methodology has been relatively neglected in the literature compared to health state valuation (for example). This paper tries to redress the balance slightly by providing an overview of the main issues in costing, explaining why they’re important, so that we can do a better job. The issues are more complex than many assume.

Supported by a formidable reference list (n=120), the authors tackle 9 issues relating to costing: i) costs vs resource use; ii) trial-based vs model-based evaluations; iii) costing perspectives; iv) data sources; v) statistical methods; vi) baseline adjustments; vii) missing data; viii) uncertainty; and ix) discounting, inflation, and currency. It’s a big paper with a lot to say, so it isn’t easily summarised. Its role is as a reference point for us to turn to when we need it. There’s a stack of papers and other resources cited in here that I wasn’t aware of. The paper itself doesn’t get technical, leaving that to the papers cited therein. But the authors provide a good discussion of the questions that ought to be addressed by somebody designing a study, relating to data collection and analysis.

The paper closes with some recommendations. The main one is that people conducting cost-effectiveness analysis should think harder about why they’re making particular methodological choices. The point is also made that new developments could change the way we collect and analyse cost data. For example, the growing use of observational data demands that greater consideration be given to unobserved confounding. Costing methods are important and interesting!

A flexible open-source decision model for value assessment of biologic treatment for rheumatoid arthritis. PharmacoEconomics [PubMed] Published 9th February 2019

Wherever feasible, decision models should be published open-source, so that they can be reviewed, reused, recycled, or, perhaps, rejected. But open-source models are still a rare sight. Here, we have one for rheumatoid arthritis. But the paper isn’t really about the model. After all, the model and supporting documentation are already available online. Rather, the paper describes the reasoning behind publishing a model open-source, and the process for doing so in this case.

This is the first model released as part of the Open Source Value Project, which tries to convince decision-makers that cost-effectiveness models are worth paying attention to. That is, it’s aimed at the US market, where models are largely ignored. The authors argue that models need to be flexible to be valuable into the future and that, to achieve this, four steps should be followed in the development: 1) release the initial model, 2) invite feedback, 3) convene an expert panel to determine actions in light of the feedback, and 4) revise the model. Then, repeat as necessary. Alongside this, people with the requisite technical skills (i.e. knowing how to use R, C++, and GitHub) can proffer changes to the model whenever they like. This paper was written after step 3 had been completed, and the authors report receiving 159 comments on their model.

The model itself (which you can have a play with here) is an individual patient simulation, which is set-up to evaluate a variety of treatment scenarios. It estimates costs and (mapped) QALYs and can be used to conduct cost-effectiveness analysis or multi-criteria decision analysis. The model was designed to be able to run 32 different model structures based on different assumptions about treatment pathways and outcomes, meaning that the authors could evaluate structural uncertainties (which is a rare feat). A variety of approaches were used to validate the model.

The authors identify several challenges that they experienced in the process, including difficulties in communication between stakeholders and the large amount of time needed to develop, test, and describe a model of this sophistication. I would imagine that, compared with most decision models, the amount of work underlying this paper is staggering. Whether or not that work is worthwhile depends on whether researchers and policymakers make us of the model. The authors have made it as easy as possible for stakeholders to engage with and build on their work, so they should be hopeful that it will bear fruit.

EQ-5D-Y-5L: developing a revised EQ-5D-Y with increased response categories. Quality of Life Research [PubMed] Published 9th February 2019

The EQ-5D-Y has been a slow burner. It’s been around 10 years since it first came on the scene, but we’ve been without a value set and – with the introduction of the EQ-5D-5L – the questionnaire has lost some comparability with its adult equivalent. But the EQ-5D-Y has almost caught-up, and this study describes part of how that’s been achieved.

The reason to develop a 5L version for the EQ-5D-Y is the same as for the adult version – to reduce ceiling effects and improve sensitivity. A selection of possible descriptors was identified through a review of the literature. Focus groups were conducted with children between 8 and 15 years of age in Germany, Spain, Sweden, and the UK in order to identify labels that can be understood by young people. Specifically, the researchers wanted to know the words used by children and adolescents to describe the quantity or intensity of health problems. Participants ranked the labels according to severity and specified which labels they didn’t like. Transcripts were analysed using thematic content analysis. Next, individual interviews were conducted with 255 participants across the four countries, which involved sorting and response scaling tasks. Younger children used a smiley scale. At this stage, both 4L and 5L versions were being considered. In a second phase of the research, cognitive interviews were used to test for comprehensibility and feasibility.

A 5-level version was preferred by most, and 5L labels were identified in each language. The English version used terms like ‘a little bit’, ‘a lot’, and ‘really’. There’s plenty more research to be done on the EQ-5D-Y-5L, including psychometric testing, but I’d expect it to be coming to studies near you very soon. One of the key takeaways from this study, and something that I’ve been seeing more in research in recent years, is that kids are smart. The authors make this point clear, particulary with respect to the response scaling tasks that were conducted with children as young as 8. Decision-making criteria and frameworks that relate to children should be based on children’s preferences and ideas.

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Chris Sampson’s journal round-up for 4th February 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.

Patient choice and provider competition – quality enhancing drivers in primary care? Social Science & Medicine Published 29th January 2019

There’s no shortage of studies in economics claiming to identify the impact (or lack of impact) of competition in the market for health care. The evidence has brought us close to a consensus that greater competition might improve quality, so long as providers don’t compete on price. However, many of these studies aren’t able to demonstrate the mechanism through which competition might improve quality, and the causality is therefore speculative. The research reported in this article was an attempt to see whether the supposed mechanisms for quality improvement actually exist. The authors distinguish between the demand-side mechanisms of competition-increasing quality-improving reforms (i.e. changes in patient behaviour) and the supply-side mechanisms (i.e. changes in provider behaviour), asserting that the supply-side has been neglected in the research.

The study is based on primary care in Sweden’s two largest cities, where patients can choose their primary care practice, which could be a private provider. Key is the fact that patients can switch between providers as often as they like, and with fewer barriers to doing so than in the UK. Prospective patients have access to some published quality indicators. With the goal of maximum variation, the researchers recruited 13 primary health care providers for semi-structured interviews with the practice manager and (in most cases) one or more of the practice GPs. The interview protocol included questions about the organisation of patient visits, information received about patients’ choices, market situation, reimbursement, and working conditions. Interview transcripts were coded and a framework established. Two overarching themes were ‘local market conditions’ and ‘feedback from patient choice’.

Most interviewees did not see competitors in the local market as a threat – conversely, providers are encouraged to cooperate on matters such as public health. Where providers did talk about competing, it was in terms of (speed of) access for patients, or in competition to recruit and keep staff. None of the interviewees were automatically informed of patients being removed from their list, and some managers reported difficulties in actually knowing which patients on their list were still genuinely on it. Even where these data were more readily available, nobody had access to information on reasons for patients leaving. Managers saw greater availability of this information as useful for quality improvement, while GPs tended to think it could be useful in ensuring continuity of care. Still, most expressed no desire to expand their market share. Managers reported using marketing efforts in response to greater competition generally, rather than as a response to observed changes within their practice. But most relied on reputation. Some reported becoming more service-minded as a result of choice reforms.

It seems that practices need more information to be able to act on competitive pressures. But, most practices don’t care about it because they don’t want to expand and they face no risk of there being a shortage of patients (in cities, at least). And, even if they did want to act on the information, chances are it would just create an opportunity for them to improve access as a way of cherry-picking younger and healthier people who demand convenience. Primary care providers (in this study, at least) are not income maximisers, but satisficers (they want to break-even), so there isn’t much scope for reforms to encourage providers to compete for new patients. Patient choice reforms may improve quality, but it isn’t clear that this has anything to do with competitive pressure.

Maximising the impact of patient reported outcome assessment for patients and society. BMJ [PubMed] Published 24th January 2019

Patient-reported outcome measures (PROMs) have been touted as a way of improving patient care. Yet, their use around the world is fragmented. In this paper, the authors make some recommendations about how we might use PROMs to improve patient care. The authors summarise some of the benefits of using PROMs and discuss some of the ways that they’ve been used in the UK.

Five key challenges in the use of PROMs are specified: i) appropriate and consistent selection of the best measures; ii) ethical collection and reporting of PROM data; iii) data collection, analysis, reporting, and interpretation; iv) data logistics; and v) a lack of coordination and efficiency. To address these challenges, the authors recommend an ‘integrated’ approach. To achieve this, stakeholder engagement is important and a governance framework needs to be developed. A handy table of current uses is provided.

I can’t argue with what the paper proposes, but it outlines an idealised scenario rather than any firm and actionable recommendations. What the authors don’t discuss is the fact that the use of PROMs in the UK is flailing. The NHS PROMs programme has been scaled back, measures have been dropped from the QOF, the EQ-5D has been dropped from the GP Patient Survey. Perhaps we need bolder recommendations and new ideas to turn the tide.

Check your checklist: the danger of over- and underestimating the quality of economic evaluations. PharmacoEconomics – Open [PubMed] Published 24th January 2019

This paper outlines the problems associated with misusing methodological and reporting checklists. The author argues that the current number of checklists available in the context of economic evaluation and HTA (13, apparently) is ‘overwhelming’. Three key issues are discussed. First, researchers choose the wrong checklist. A previous review found that the Drummond, CHEC, and Philips checklists were regularly used in the wrong context. Second, checklists can be overinterpreted, resulting in incorrect conclusions. A complete checklist does not mean that a study is perfect, and different features are of varying importance in different studies. Third, checklists are misused, with researchers deciding which items are or aren’t relevant to their study, without guidance.

The author suggests that more guidance is needed and that a checklist for selecting the correct checklist could be the way to go. The issue of updating checklists over time – and who ought to be responsible for this – is also raised.

In general, the tendency seems to be to broaden the scope of general checklists and to develop new checklists for specific methodologies, requiring the application of multiple checklists. As methods develop, they become increasingly specialised and heterogeneous. I think there’s little hope for checklists in this context unless they’re pared down and used as a reminder of the more complex guidance that’s needed to specify suitable methods and achieve adequate reporting. ‘Check your checklist’ is a useful refrain, though I reckon ‘chuck your checklist’ can sometimes be a better strategy.

A systematic review of dimensions evaluating patient experience in chronic illness. Health and Quality of Life Outcomes [PubMed] Published 21st January 2019

Back to PROMs and PRE(xperience)Ms. This study sets out to understand what it is that patient-reported measures are being used to capture in the context of chronic illness. The authors conducted a systematic review, screening 2,375 articles and ultimately including 107 articles that investigated the measurement properties of chronic (physical) illness PROMs and PREMs.

29 questionnaires were about (health-related) quality of life, 19 about functional status or symptoms, 20 on feelings and attitudes about illness, 19 assessing attitudes towards health care, and 20 on patient experience. The authors provide some nice radar charts showing the percentage of questionnaires that included each of 12 dimensions: i) physical, ii) functional, iii) social, iv) psychological, v) illness perceptions, vi) behaviours and coping, vii) effects of treatment, viii) expectations and satisfaction, ix) experience of health care, x) beliefs and adherence to treatment, xi) involvement in health care, and xii) patient’s knowledge.

The study supports the idea that a patient’s lived experience of illness and treatment, and adaptation to that, has been judged to be important in addition to quality of life indicators. The authors recommend that no measure should try to capture everything because there are simply too many concepts that could be included. Rather, researchers should specify the domains of interest and clearly define them for instrument development.

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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.