Sam Watson’s journal round-up for 21st August 2017

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.

Multidimensional performance assessment of public sector organisations using dominance criteria. Health Economics [RePEcPublished 18th August 2017

The empirical assessment of the performance or quality of public organisations such as health care providers is an interesting and oft-tackled problem. Despite the development of sophisticated methods in a large and growing literature, public bodies continue to use demonstrably inaccurate or misleading statistics such as the standardised mortality ratio (SMR). Apart from the issue that these statistics may not be very well correlated with underlying quality, organisations may improve on a given measure by sacrificing their performance on another outcome valued by different stakeholders. One example from a few years ago showed how hospital rankings based upon SMRs shifted significantly if one took into account readmission rates and their correlation with SMRs. This paper advances this thinking a step further by considering multiple outcomes potentially valued by stakeholders and using dominance criteria to compare hospitals. A hospital dominates another if it performs at least as well or better across all outcomes. Importantly, correlation between these measures is captured in a multilevel model. I am an advocate of this type of approach, that is, the use of multilevel models to combine information across multiple ‘dimensions’ of quality. Indeed, my only real criticism would be that it doesn’t go far enough! The multivariate normal model used in the paper assumes a linear relationship between outcomes in their conditional distributions. Similarly, an instrumental variable model is also used (using the now routine distance-to-health-facility instrumental variable) that also assumes a linear relationship between outcomes and ‘unobserved heterogeneity’. The complex behaviour of health care providers may well suggest these assumptions do not hold – for example, failing institutions may well show poor performance across the board, while other facilities are able to trade-off outcomes with one another. This would suggest a non-linear relationship. I’m also finding it hard to get my head around the IV model: in particular what the covariance matrix for the whole model is and if correlations are permitted in these models at multiple levels as well. Nevertheless, it’s an interesting take on the performance question, but my faith that decent methods like this will be used in practice continues to wane as organisations such as Dr Foster still dominate quality monitoring.

A simultaneous equation approach to estimating HIV prevalence with nonignorable missing responses. Journal of the American Statistical Association [RePEcPublished August 2017

Non-response is a problem encountered more often than not in survey based data collection. For many public health applications though, surveys are the primary way of determining the prevalence and distribution of disease, knowledge of which is required for effective public health policy. Methods such as multiple imputation can be used in the face of missing data, but this requires an assumption that the data are missing at random. For disease surveys this is unlikely to be true. For example, the stigma around HIV may make many people choose not to respond to an HIV survey, thus leading to a situation where data are missing not at random. This paper tackles the question of estimating HIV prevalence in the face of informative non-response. Most economists are familiar with the Heckman selection model, which is a way of correcting for sample selection bias. The Heckman model is typically estimated or viewed as a control function approach in which the residuals from a selection model are used in a model for the outcome of interest to control for unobserved heterogeneity. An alternative way of representing this model is as copula between a survey response variable and the response variable itself. This representation is more flexible and permits a variety of models for both selection and outcomes. This paper includes spatial effects (given the nature of disease transmission) not only in the selection and outcomes models, but also in the model for the mixing parameter between the two marginal distributions, which allows the degree of informative non-response to differ by location and be correlated over space. The instrumental variable used is the identity of the interviewer since different interviewers are expected to be more or less successful at collecting data independent of the status of the individual being interviewed.

Clustered multistate models with observation level random effects, mover–stayer effects and dynamic covariates: modelling transition intensities and sojourn times in a study of psoriatic arthritis. Journal of the Royal Statistical Society: Series C [ArXiv] Published 25th July 2017

Modelling the progression of disease accurately is important for economic evaluation. A delicate balance between bias and variance should be sought: a model too simple will be wrong for most people, a model too complex will be too uncertain. A huge range of models therefore exists from ‘simple’ decision trees to ‘complex’ patient-level simulations. A popular choice are multistate models, such as Markov models, which provide a convenient framework for examining the evolution of stochastic processes and systems. A common feature of such models is the Markov property, which is that the probability of moving to a given state is independent of what has happened previously. This can be relaxed by adding covariates to model transition properties that capture event history or other salient features. This paper provides a neat example of extending this approach further in the case of arthritis. The development of arthritic damage in a hand joint can be described by a multistate model, but there are obviously multiple joints in one hand. What is more, the outcomes in any one joint are not likely to be independent of one another. This paper describes a multilevel model of transition probabilities for multiple correlated processes along with other extensions like dynamic covariates and different mover-stayer probabilities.

Credits

#HEJC for 03/06/2013

This month’s meeting will take place Monday 3rd June, at 5pm London time. That’ll be midday in Boston and 6pm in Geneva. Join the Facebook event here. We’ll also hold an antipodal meeting 12 hours later on Tuesday 4th June, at 5am London time. That’ll be midday in Beijing and 6pm on Monday in Honolulu. Join the Facebook event here. For more information about the Health Economics Twitter Journal Club and how to take part, click here.

The paper for discussion this month is a working paper published by the National Bureau of Economic Research. The authors are Janet Currie and W. Bentley MacLeodThe title of the paper is:

“Diagnosis and unnecessary procedure use: evidence from C-section”

Following the meeting, a transcript of the discussion can be downloaded here.

Links to the article

Direct: http://www.nber.org/papers/w18977

RePEc: http://ideas.repec.org/p/nbr/nberwo/18977.html

Other: tbc

Summary of the paper

In this paper the authors develop a model of diagnostic skill as an element of provider quality that is separate from a doctor’s skill in performing procedures. The model shows that higher surgical skill leads to higher use of surgical procedures across all patients, while better diagnostic skill results in fewer procedures for the low risk and more procedures for the high risk. When doctors face a dichotomous choice between an intensive and a non-intensive procedure they have a threshold level of patient condition; above which patients receive the intensive procedure and below which they receive the non-intensive procedure. The doctor’s threshold level is dependent on their surgical skill and the pecuniary benefit associated with carrying out the procedure. Greater diagnostic skill improves the precision of the doctor’s estimate of a patient’s condition and therefore improves the matching between patients and procedures; leading to better health outcomes. Taking the model to data on C-sections, the most common surgical procedure performed in the U.S., the authors show that improving diagnostic skills from the 25th to the 75th percentile of the observed distribution would reduce C-section rates by 11.7% among the low risk, and increase them by 4.6% among the high risk. Since there are many more low risk than high risk women, improving diagnosis would reduce overall C-section rates by about 5% of total births. Moreover, such an improvement in diagnostic skill would improve health outcomes for both high risk and low risk women, while improvements in surgical skill have the greatest impact on high risk women. The results are consistent with the hypothesis that efforts to improve diagnosis through methods such as checklists, computer assisted diagnosis, and collaborative decision making may improve patient outcomes.

Discussion points

  • Are there other aspects of physician skill that could be estimated in this way?
  • Is the characterisation of a doctor’s payoffs accurate?
  • To what other procedures could the model be applied?
  • To what extent could this model inform non-dichotomous physician decisions?
  • What are the key policy implications of these findings?

Missed the meeting? Add your thoughts on the paper in the comments below.