Analysing Patient-Level Data using HES Workshop

This intensive workshop introduces participants to HES (Hospital Episode Statistics) data and how to handle and manipulate these very large patient-level data sets using computer software. Understanding and interpreting the data is a key first step for using these data in economic evaluation or evaluating health care policy and practice. Participants will engage in lectures and problem-solving exercises, analysing the information in highly interactive sessions. Data manipulation and statistical analysis will be taught and demonstrated using Stata.

This workshop is offered to people in the academic, public and commercial sectors.  It is useful for analysts who wish to harness the power of HES non-randomised episode level patient data to shed further light on such things as patient costs and pathways, re-admissions and outcomes and provider performance.  The workshop is suitable for individuals working in NHS hospitals, commissioning organisations, NHS England, Monitor, and the Department of Health and Social Care, pharmaceutical companies or consultancy companies and for health care researchers and PhD students.  Overseas participants may find the tuition helpful for their own country, but note that the course is heavily oriented towards understanding HES data for England.

The workshop fee is 900GBP for the public sector; 1,400GBP for the commercial sector. This includes all tuition, course materials, lunches, the welcome and drinks reception, the workshop dinner and refreshments, but does not include accommodation.

Online registration is now open; further information and registration is at: https://www.york.ac.uk/che/courses/patient-data/

Subsidised places are available for full-time PhD students. If this is applicable to you, please email the workshop administrators and request an Application Form.

Contact: Gillian or Louise, Workshop Administrators, at: che-apd@york.ac.uk;  tel: +44 (0)1904 321436

OHE Lunchtime Seminar: An Overview of the US National Institutes of Health (NIH) Patient Reported Outcomes Measurement Information System (PROMIS®) and the PROMIS-Preference (PROPr) Scoring System

The EQ-5D, Health Utilities Index, SF-6D, and the Quality of Well-being Index are among the most widely used generic preference-based health-related quality of life measures. However, they each have some of the following limitations: (1) large proportions of the respondents scoring at the very top or very bottom of the scale in some populations of interest (i.e., ceiling effects in the very healthy or floor effects in the very ill), (2) imprecise measurement for individuals, (3) poorly-worded questions such as those that combine two concepts (double-barreled questions), and (4) differences in range of domains covered. While modification of a particular instrument may overcome some of these problems, modification also results in concerns about comparability of results obtained with different versions of the same instrument.
The US NIH Patient Reported Outcomes Measurement Information System (PROMIS®) provides an opportunity to address several limitations of the existing generic preference measures including: (1) fully capturing the entire range of a construct, (2) measuring an individual’s health status with greater precision, and (3) creating a standardised valuation methodology for future studies. The PROMIS® measures stand to be highly applicable across clinical, research, and population studies. Thus, creating a preference-based scoring system for PROMIS would allow efficient use of study resources to collect both health profile and health utility scores.
Combining both psychometric theory (item-response theory; IRT) and econometric theory (multi-attribute utility theory; MAUT), Janel Hanmer will discuss the creation of the PROMIS-Preference (PROPr) scoring system.  She will discuss how IRT is used to create measures of health domains, the linking of IRT calibrated questions to MAUT scoring, and the resulting scoring system.
Janel Hanmer is an Assistant Professor in the Department of Medicine at the University of Pittsburgh.  She is also the Medical Director for Patient Reported Outcomes at the University of Pittsburgh Medical Center. She leads the effort to develop the PROPr scoring system.
If you would like to attend this seminar, please reply to ohegeneral@ohe.org.
Click here to see the full invite.

Webinar facilities will also be available for this lunchtime seminar, however registration is needed. Please send an email to ohegeneral@ohe.org if you wish to join. Details of the webinar will be sent out closer to the event date.

Chris Sampson’s journal round-up for 5th March 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.

Healthy working days: the (positive) effect of work effort on occupational health from a human capital approach. Social Science & Medicine Published 28th February 2018

If you look at the literature on the determinants of subjective well-being (or happiness), you’ll see that unemployment is often cited as having a big negative impact. The same sometimes applies for its impact on health, but here – of course – the causality is difficult to tease apart. Then, in research that digs deeper, looking at hours worked and different types of jobs, we see less conclusive results. In this paper, the authors start by asserting that the standard approach in labour economics (on which I’m not qualified to comment) is to assume that there is a negative association between work effort and health. This study extends the framework by allowing for positive effects of work that are related to individuals’ characteristics and working conditions, and where health is determined in a Grossman-style model of health capital that accounts for work effort in the rate of health depreciation. This model is used to examine health as a function of work effort (as indicated by hours worked) in a single wave of the European Working Conditions Survey (EWCS) from 2010 for 15 EU member states. Key items from the EWCS included in this study are questions such as “does your work affect your health or not?”, “how is your health in general?”, and “how many hours do you usually work per week?”. Working conditions are taken into account by looking at data on shift working and the need to wear protective equipment. One of the main findings of the study is that – with good working conditions – greater work effort can improve health. The Marxist in me is not very satisfied with this. We need to ask the question, compared to what? Working fewer hours? For most people, that simply isn’t an option. Aren’t the people who work fewer hours the people who can afford to work fewer hours? No attention is given to the sociological aspects of employment, which are clearly important. The study also shows that overworking or having poorer working conditions reduces health. We also see that, for many groups, longer hours do not negatively impact on health until we reach around 120 hours a week. This fails a good sense check. Who are these people?! I’d be very interested to see if these findings hold for academics. That the key variables are self-reported undermines the conclusions somewhat, as we can expect people to adjust their expectations about work effort and health in accordance with their colleagues. It would be very difficult to avoid a type 2 error (with respect to the negative impact of effort on health) using these variables to represent health and the role of work effort.

Agreement between retrospectively and contemporaneously collected patient-reported outcome measures (PROMs) in hip and knee replacement patients. Quality of Life Research [PubMed] Published 26th February 2018

The use of patient-reported outcomes (PROMs) in elective care in the NHS has been a boon for researchers in our field, providing before-and-after measurement of health-related quality of life so that we can look at the impact of these interventions. But we can’t do this in emergency care because the ‘before’ is never observed – people only show up when they’re in the middle of the emergency. But what if people could accurately recall their pre-emergency health state? There’s some evidence to suggest that people can, so long as the recall period is short. This study looks at NHS PROMs data (n=443), with generic and condition-specific outcomes collected from patients having hip or knee replacements. Patients included in the study were additionally asked to recall their health state 4 weeks prior to surgery. The authors assess the extent to which the contemporary PROM measurements agree with the retrospective measurements, and the extent to which any disagreement relates to age, socioeconomic status, or the length of time to recall. There wasn’t much difference between contemporary and retrospective measurements, though patients reported slightly lower health on the retrospective questionnaires. And there weren’t any compelling differences associated with age or socioeconomic status or the length of recall. These findings are promising, suggesting that we might be able to rely on retrospective PROMs. But the elective surgery context is very different to the emergency context, and I don’t think we can expect the two types of health care to impact recollection in the same way. In this study, responses may also have been influenced by participants’ memories of completing the contemporary questionnaire, and the recall period was very short. But the only way to find out more about the validity of retrospective PROM collection is to do more of it, so hopefully we’ll see more studies asking this question.

Adaptation or recovery after health shocks? Evidence using subjective and objective health measures. Health Economics [PubMed] Published 26th February 2018

People’s expectations about their health can influence their behaviour and determine their future health, so it’s important that we understand people’s expectations and any ways in which they diverge from reality. This paper considers the effect of a health shock on people’s expectations about how long they will live. The authors focus on survival probability, measured objectively (i.e. what actually happens to these patients) and subjectively (i.e. what the patients expect), and the extent to which the latter corresponds to the former. The arguments presented are couched within the concept of hedonic adaptation. So the question is – if post-shock expectations return to pre-shock expectations after a period of time – whether this is because people are recovering from the disease or because they are moving their reference point. Data are drawn from the Health and Retirement Study. Subjective survival probability is scaled to whether individuals expect to survive for 2 years. Cancer, stroke, and myocardial infarction are the health shocks used. The analysis uses some lagged regression models, separate for each of the three diagnoses, with objective and subjective survival probability as the dependent variable. There’s a bit of a jumble of things going on in this paper, with discussions of adaptation, survival, self-assessed health, optimism, and health behaviours. So it’s a bit difficult to see the wood for the trees. But the authors find the effect they’re looking for. Objective survival probability is negatively affected by a health shock, as is subjective survival probability. But then subjective survival starts to return to pre-shock trends whereas objective survival does not. The authors use this finding to suggest that there is adaptation. I’m not sure about this interpretation. To me it seems as if subjective life expectancy is only weakly responsive to changes in objective life expectancy. The findings seem to have more to do with how people process information about their probability of survival than with how they adapt to a situation. So while this is an interesting study about how people process changes in survival probability, I’m not sure what it has to do with adaptation.

3L, 5L, what the L? A NICE conundrum. PharmacoEconomics [PubMed] Published 26th February 2018

In my last round-up, I said I was going to write a follow-up blog post to an editorial on the EQ-5D-5L. I didn’t get round to it, but that’s probably best as there has since been a flurry of other editorials and commentaries on the subject. Here’s one of them. This commentary considers the perspective of NICE in deciding whether to support the use of the EQ-5D-5L and its English value set. The authors point out the differences between the 3L and 5L, namely the descriptive systems and the value sets. Examples of the 5L descriptive system’s advantages are provided: a reduced ceiling effect, reduced clustering, better discriminative ability, and the benefits of doing away with the ‘confined to bed’ level of the mobility domain. Great! On to the value set. There are lots of differences here, with 3 main causes: the data, the preference elicitation methods, and the modelling methods. We can’t immediately determine whether these differences are improvements or not. The authors stress the point that any differences observed will be in large part due to quirks in the original 3L value set rather than in the 5L value set. Nevertheless, the commentary is broadly supportive of a cautionary approach to 5L adoption. I’m not. Time for that follow-up blog post.

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