Sam Watson’s journal round-up for 11th December 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.

Can incentives improve survey data quality in developing countries?: results from a field experiment in India. Journal of the Royal Statistical Society: Series A Published 17th November 2017

I must admit a keen interest in the topic of this paper. As part of a large project looking at the availability of health services in slums and informal settlements around the world, we are designing a household survey. Much like the Demographic and Health Surveys, which are perhaps the Gold standard of household surveys in low-income countries, interviewers will go door to door to sampled households to complete surveys. One of the issues with household surveys is that they take a long time, and so non-response can be an issue. A potential solution is to offer respondents incentives, cash or otherwise, either before the survey or conditionally on completing it. But any change in survey response as a result of an incentive might create suspicion around data quality. Work in high-income countries suggests incentives to participate have little or no effect on data quality. But there is little evidence about these effects in low-income countries. We might suspect the consequences of survey incentives to differ in poorer settings. For a start, many surveys are conducted on behalf of the government or an NGO, and respondents may misrepresent themselves if they believe further investment in their area might be forthcoming if they are sufficiently badly-off. There may also be larger differences between the interviewer and interviewee in terms of education or cultural background. And finally, incentives can affect the balance between a respondent’s so-called intrinsic and extrinsic motivations for doing something. This study presents the results of a randomised trial where the ‘treatment’ was a small conditional payment for completing a survey, and the ‘control’ was no incentive. In both arms, the response rate was very high (>96%), but it was higher in the treatment arm. More importantly, the authors compare responses to a broad range of socioeconomic and demographic questions between the study arms. Aside from the frequent criticism that statistical significance is interpreted here as the existence of a difference, there are some interesting results. The key observed difference is that in the incentive arm respondents reported having lower wealth consistently across a number of categories. This may result from any of the aforementioned effects of incentives, but may be evidence that incentives can affect data quality and should be used with caution.

Association of US state implementation of newborn screening policies for critical congenital heart disease with early infant cardiac deaths. JAMA [PubMedPublished 5th December 2017

Writing these journal round-ups obviously requires reading the papers that you choose. This can be quite an undertaking for papers published in economics journals, which are often very long, but they provide substantial detail allowing for a thorough appraisal. The opposite is true for articles in medical journals. They are pleasingly concise, but often at the expense of including detail or additional analyses. This paper falls into the latter camp. Using detailed panel data on infant deaths by cause by year and by state in the US, it estimates the effect of mandated screening policies for infant congenital heart defects on deaths from this condition. Given these data and more space, one might expect to see more flexible models than the differences in differences type analysis presented here, such as allowing for state-level correlated time trends. The results seem clear and robust – the policies were associated with a reduction in death from congenital heart conditions by around a third. Given this, one might ask: if it’s so effective, why weren’t doctors doing it anyway? Additional analyses reveal little to no association of the policies with death from other conditions, which may suggest that doctors didn’t have to reallocate their time from other beneficial functions. Perhaps then the screening bore other costs. In the discussion, the authors mention that a previous economic evaluation showed that universal screening was relatively costly (approximately $40,000 per life year saved), but that this may be an overestimate in light of these new results. Certainly then an updated economic evaluation is warranted. However, the models used in the paper may lead one to be cautious about causal interpretations and hence using the estimates in an evaluation. Given some more space the authors may have added additional analyses, but then I might not have read it…

Subsidies and structure: the lasting impact of the Hill-Burton program on the hospital industry. Review of Economics and Statistics [RePEcPublished 29th November 2017

As part of the Hospital Survey and Construction Act of 1946 in the United States, the Hill-Burton program was enacted. As a reaction to the perceived lack of health care services for workers during World War 2, the program provided subsidies of up to a third for building nonprofit and local hospitals. Poorer areas were prioritised. This article examines the consequences of this subsidy program on the structure of the hospital market and health care utilisation. The main result is that the program had the consequence of increasing hospital beds per capita and that this increase was lasting. More specific analyses are presented. Firstly, the increase in beds took a number of years to materialise and showed a dose-response; higher-funded counties had bigger increases. Secondly, the funding reduced private hospital bed capacity. The net effect on overall hospital beds was positive, so the program affected the composition of the hospital sector. Although this would be expected given that it substantially affected the relative costs of different types of hospital bed. And thirdly, hospital utilisation increased in line with the increases in capacity, indicating a previously unmet need for health care. Again, this was expected given the motivation for the program in the first place. It isn’t often that results turn out as neatly as this – the effects are exactly as one would expect and are large in magnitude. If only all research projects turned out this way.

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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.

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Alastair Canaway’s journal round-up for 12th September 2016

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.

Question order sensitivity of subjective well-being measures: focus on life satisfaction, self-rated health, and subjective life expectancy in survey instruments. Quality of Life Research [PubMed] Published 30th April 2016

It’s interesting to see an ‘old’ and well known issue rearing its head within the health economics literature. In this case, the focus is on ordering bias within wellbeing questionnaires. It is established within the psychometric and psychological literature that the location of a question within a survey can influence how respondents interpret the meaning of the question, and therefore their answers. This study sought to empirically examine how ordering in subjective well-being measures (life satisfaction, self-rated health, and subjective life expectancy) affected answers. Given ordering bias is an established concept, it wasn’t too surprising to find notable ordering bias depending on how the questionnaire was ordered. For example, as hypothesised by the authors, placing self-rated health immediately before life satisfaction within the survey led to different values compared to when placed apart. For well-being research, the paper has important implications, particularly in how to best order questionnaires to reduce the impact of prior questions on answers, e.g. keeping self-rated health and life satisfaction questions apart to encourage respondents to independently evaluate each question. Ordering bias is one of those issues that most researchers are aware of, but tend to forget about. As much as anything I feel this is for pragmatic reasons, for example, in terms of ease of producing case report forms and also for facilitating data entry within trials. Ideally, we probably should be randomising the order of questionnaires, whether we can persuade wider trial teams that this is necessary remains to be seen.

You sneeze, you lose: The impact of pollen exposure on cognitive performance during high-stakes high school exams. Journal of Health Economics [PubMed] [RePEcPublished September 2016

As a ‘summer sneezer’ and someone with poor exam results in year 9, it was of great interest to read this article. It is known that health and productivity are intrinsically linked, indeed productivity costs related to health are commonly discussed within health economics circles. Elsewhere there are studies that have identified pollution levels as having significant effects on labour productivity and supply. As any fellow hay fever (seasonal allergic rhinitis) sufferers will attest, hay fever has a direct negative impact on wellbeing. Hay fever is relatively prevalent with over one in five people being reported to suffer (in the Norwegian setting at least). This study combined a large administrative dataset from the Norwegian high school system with daily pollen counts from measurement stations across Norway. Student exam data were matched with location of exams and the pollen count for the area in which the exam took place. Fixed effect panel data methods were used to analyse the data. The primary result found that one standard deviation increase in pollen levels led to a decrease in a student’s exam score by about 2.5% of a standard deviation, the implication of this is that for allergic students, this negative effect is approximately 10% of a standard deviation. This is a notable margin. The paper has an interesting discussion on the potential long term impact of hay fever on allergic students, and their future prospects e.g. impact on university enrolment. To avoid such impacts the paper emphasises the need to diagnose early and optimise treatment for hay fever in children. One final point (and word of caution) would be that the methods don’t prove causality, however as a hay fever sufferer, it was very interesting nonetheless to consider how the condition may have impacted upon my own performance at school.

The fatter are happier in Indonesia. Quality of Life Research [PubMed] Published 31st August 2016

An eye-catching title. In developed countries, being overweight and obese typically has negative connotations, and studies repeatedly suggest this is the case: those who are overweight are less happy. In developing countries however, this is not necessarily true. The paper offers the following reason for this: wealth and obesity are positively correlated in such countries, and likewise, happiness and wealth are positively related. Those who are poor in developing countries literally cannot afford to be obese. In contrast, in developed countries, even lower socioeconomic classes can afford to be obese (and obesity is indeed more prevalent in these classes). With this in mind, this study sought to determine how obesity and happiness were related in Indonesia. The study used a large long term survey of over 22,000 participants over a long time period. As hypothesised, the study found there to be a positive association between obesity and self-reported happiness within Indonesia. The paper in a roundabout way suggests that a different approach to evaluating obesity prevention is required in the developing world. I’m not sure this is necessarily the case, in my experience it is rare to assess obesity prevention interventions with respect to ‘happiness’. It takes me back to a previous journal round-up discussing the maximand within economic evaluation. Obesity, if not immediately, eventually is associated with poor health, therefore there is nothing to suggest that an evaluative framework that seeks to maximise health over happiness will not be sufficient. There are many issues related to the long term evaluation of obesity prevention interventions, particularly those focussed in children (as outlined here), however I think the case stated in this paper is a bit of red herring.

Photo credit: Antony Theobald (CC BY-NC-ND 2.0)