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The journal Health Economics has recently launched a special online-only issue, which brings together previously published studies related to pandemics. Current events are sure to have sparked new interest in these studies. In this spirit, I have selected studies from this special issue as well as another prior pandemic study for this week’s journal round-up.
Excess influenza hospital admissions and costs due to the 2009 H1N1 pandemic in England. Health Economics [PubMed] [RePEc] Published February 2019
One feature of coronavirus pandemic news reporting is the near real-time update on numbers of infections, hospital admissions, and deaths. These reports have been followed by a confusing set of revisions, corrections, and alternative data sources. The introduction section of this paper shows that interpretation of past pandemics can be no less challenging. Previous estimates of excess hospital admission and costs from the H1N1 influenza pandemic varied 10-fold, even within the same healthcare system.
In this relatively recent study the authors attempted to estimate, more robustly, the impact of H1N1 on the healthcare system in England in terms of hospital admissions and hospital costs. The data used in the study were comprehensive. Hospital Episode Statistics (HES) were available for all hospitals for the period 2004-2011, including the 2009/2010 pandemic period.
Records of possible H1N1 admissions were identified using influenza-like-illness (ILI) ICD-10 codes (n≈30,000). Costing of admissions used matched HRG codes. The effect of the pandemic virus was estimated by constructing counterfactual outcomes for comparison. Expected admissions and costs for pandemic weeks in the absence of a pandemic were estimated using a set of seasonal autoregressive integrated moving average (SARIMA) models.
The total hospital costs of the pandemic in the UK are reported to be approximately £45 million. The authors conclude that previous studies both over- and under-estimated these costs by either overcounting or undercounting respectively. They then take the bold step of providing ‘corrected’ estimates for each previous study. I admire the author’s confidence. However, while I believe their estimates are more accurate, I am not certain that treating them as ‘ground truth’ and estimating the precise degree of bias of previous studies is such a good idea.
One other curious feature also stood out in this paper; the incredible year-to-year variation in ILI admission even in non-pandemic years. Different data inclusion and modelling practices could produce justifiable but different results. In assessing admissions and costs of the present pandemic, I hope we can gain a deeper understanding of these types of data.
To vaccinate or to procrastinate? That is the prevention question. Health Economics [PubMed] [RePEc] Published December 2016
Vaccination is another hot topic for coronavirus. High uptake of vaccines is important for achieving the additional benefits from herd immunity. In most countries, uptake of the annual influenza vaccines is at less than target levels. This paper investigated explanatory factors from an economist’s perspective, viewing the decision to vaccinate as an investment.
Three important features of the decision are specialist knowledge, uncertainty, and the inter-temporal nature of the outcomes. Therefore, candidate explanatory factors are information, risk preferences, and time preferences, respectively. The authors provide an interesting discussion of risk and time preferences as well as a simple model of decision making that includes both features. Data with which to test the explanatory power of the various candidate factors come from a cross-sectional survey conducted within a large German health insurance scheme, which included questions relevant for each factor as well as vaccination status.
Ultimately, only information showed strong predictive power. In particular, knowledge that vaccines could not cause the virus and that side-effects, if any, were minor. Other factors only predicted vaccination uptake when the analysis was stratified by gender. As stratification by gender was a post-hoc analysis, and not really consistent with the structural model, these findings seem more speculative.
I think this result highlights the importance of information asymmetry between experts and the public in explaining relative success or failure of public health measures. While I doubt that anyone will lack strong motivation to vaccinate for coronavirus, lack of information or misinformation can still be a powerful barrier.
Oseltamivir for influenza in adults and children: systematic review of clinical study reports and summary of regulatory comments. BMJ [PubMed] Published April 2014
Transparency is the chief virtue of economic evaluation in health. This paper highlights the importance of transparency in the pandemic context. An abundance of clinical trials and other studies are currently ongoing for treatment and prevention of COVID-19. Economic evaluation of these strategies will be required to inform decisions about their implementation in the near future.
Oseltamivir is a drug that has been stockpiled by many countries in preparation for pandemic influenza. The total costs of stockpiles run into the billions. Despite evident popularity, the efficacy of this practice is highly uncertain. The evidence base has been the subject of major controversy and revision. Many oseltamivir trials were not published at completion. The results only became available years later after systematic reviewers gained access to clinical study reports through requests to regulatory agencies. For a more complete history see this recent BMJ piece.
This systematic review combined data from published randomised studies as well as unpublished clinical trial reports. In contrast to previous more positive reviews, this more comprehensive study found no evidence for an effect on hospitalisation, serious influenza complications, or death. Issues were also identified in the quality of the evidence. Among the 23 included studies there was a high prevalence of design features that put the study at a high risk of bias (e.g. only half the studies reported adequate allocation concealment). Economic modelling of COVID-19 must ensure it is informed by the totality of the evidence. The pace of research may lead to compromises in quality. Incentives for publication bias will be as great as ever. Transparency in both the clinical evidence and economic modelling remains the key in avoiding costly mistakes.
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