Design and analysis of three-arm parallel cluster randomized trials with small numbers of clusters.

In this article, we review and evaluate a number of methods used in the design and analysis of small three-arm parallel cluster randomized trials. We conduct a simulation-based study to evaluate restricted randomization methods including covariate-constrained randomization and a novel method for matched-group cluster randomization. We also evaluate the appropriate modelling of the data and small sample inferential methods for a variety of treatment effects relevant to three-arm trials. Our results indicate that small-sample corrections are required for high (0.05) but not low (0.001) values of the intraclass correlation coefficient and their performance can depend on trial design, number of clusters, and the nature of the hypothesis being tested. The Satterthwaite correction generally performed best at an ICC of 0.05 with a nominal type I error rate for single-period trials, and in trials with repeated measures type I error rates were between 0.04 and 0.06. Restricted randomization methods produce little benefit in trials with repeated measures but in trials with single post-intervention design can provide relatively large gains in power when compared to the most unbalanced possible allocations. Matched-group randomization improves power but is not as effective as covariate-constrained randomization. For model-based analysis, adjusting for fewer covariates than were used in a restricted randomization process under any design can produce non-nominal type I error rates and reductions in power. Where comparisons to two-arm cluster trials are possible, the performance of the methods is qualitatively very similar.

The impact of varying cluster size in cross-sectional stepped-wedge cluster randomised trials.

BACKGROUND: Cluster randomised trials with unequal sized clusters often have lower precision than with clusters of equal size. To allow for this, sample sizes are inflated by a modified version of the design effect for clustering. These inflation factors are valid under the assumption that randomisation is stratified by cluster size. We investigate the impact of unequal cluster size when that constraint is relaxed, with particular focus on the stepped-wedge cluster randomised trial, where this is more difficult to achieve. METHODS: Assuming a multi-level mixed effect model with exchangeable correlation structure for a cross-sectional design, we use simulation methods to compare the precision for a trial with clusters of unequal size to a trial with clusters of equal size (relative efficiency). For a range of scenarios we illustrate the impact of various design features (the cluster-mean correlation - a function of the intracluster correlation and the cluster size, the number of clusters, number of randomisation sequences) on the average and distribution of the relative efficiency. RESULTS: Simulations confirm that the average reduction in precision, due to varying cluster sizes, is smaller in a stepped-wedge trial compared to the parallel trial. However, the variance of the distribution of the relative efficiency is large; and is larger under the stepped-wedge design compared to the parallel design. This can result in large variations in actual power, depending on the allocation of clusters to sequences. Designs with larger variations in cluster sizes, smaller number of clusters and studies with smaller cluster-mean correlations (smaller cluster sizes or smaller intra-cluster correlation) are particularly at risk. CONCLUSION: The actual realised power in a stepped-wedge trial might be substantially higher or lower than that estimated. This is particularly important when there are a small number of clusters or the variability in cluster sizes is large. Constraining the randomisation on cluster size, where feasible, might mitigate this effect.

Relative efficiency of unequal cluster sizes in stepped wedge and other trial designs under longitudinal or cross-sectional sampling.

BACKGROUND: A cluster trial with unequal cluster sizes often has lower precision than one with equal clusters, with a corresponding inflation of the design effect. For parallel group trials, adjustments to the design effect are available under sampling models with a single intracluster correlation. Design effects for equal clusters under more complex scenarios have appeared recently (including stepped wedge trials under cross-sectional or longitudinal sampling). We investigate the impact of unequal cluster size in these more general settings. RESULTS: Assuming a linear mixed model with an exchangeable correlation structure that incorporates cluster and subject autocorrelation, we compute the relative efficiency (RE) of a trial with clusters of unequal size under a size-stratified randomization scheme, as compared to an equal cluster trial with the same total number of observations. If there are no within-cluster time effects, the RE exceeds that for a parallel trial. In general, the RE is a weighted average of the RE for a parallel trial and the RE for a crossover trial in the same clusters. Existing approximations for parallel designs are extended to the general setting. Increasing the cluster size by the factor (1 + CV2 ), where CV is the coefficient of variation of cluster size, leads to conservative sample sizes, as in a popular method for parallel trials. CONCLUSION: Methods to assess experimental precision for single-period parallel trials with unequal cluster sizes can be extended to stepped wedge and other complete layouts under longitudinal or cross-sectional sampling. In practice, the loss of precision due to unequal cluster sizes is unlikely to exceed 12%.

Weekend specialist intensity and admission mortality in acute hospital trusts in England: a cross-sectional study.

BACKGROUND: Increased mortality rates associated with weekend hospital admission (the so-called weekend effect) have been attributed to suboptimum staffing levels of specialist consultants. However, evidence for a causal association is elusive, and the magnitude of the weekend specialist deficit remains unquantified. This uncertainty could hamper efforts by national health systems to introduce 7 day health services. We aimed to examine preliminary associations between specialist intensity and weekend admission mortality across the English National Health Service. METHODS: Eligible hospital trusts were those in England receiving unselected emergency admissions. On Sunday June 15 and Wednesday June 18, 2014, we undertook a point prevalence survey of hospital specialists (consultants) to obtain data relating to the care of patients admitted as emergencies. We defined specialist intensity at each trust as the self-reported estimated number of specialist hours per ten emergency admissions between 0800 h and 2000 h on Sunday and Wednesday. With use of data for all adult emergency admissions for financial year 2013-14, we compared weekend to weekday admission risk of mortality with the Sunday to Wednesday specialist intensity ratio within each trust. We stratified trusts by size quintile. FINDINGS: 127 of 141 eligible acute hospital trusts agreed to participate; 115 (91%) trusts contributed data to the point prevalence survey. Of 34,350 clinicians surveyed, 15,537 (45%) responded. Substantially fewer specialists were present providing care to emergency admissions on Sunday (1667 [11%]) than on Wednesday (6105 [42%]). Specialists present on Sunday spent 40% more time caring for emergency patients than did those present on Wednesday (mean 5·74 h [SD 3·39] vs 3·97 h [3·31]); however, the median specialist intensity on Sunday was only 48% (IQR 40-58) of that on Wednesday. The Sunday to Wednesday intensity ratio was less than 0·7 in 104 (90%) of the contributing trusts. Mortality risk among patients admitted at weekends was higher than among those admitted on weekdays (adjusted odds ratio 1·10, 95% CI 1·08-1·11; p<0·0001). There was no significant association between Sunday to Wednesday specialist intensity ratios and weekend to weekday mortality ratios (r -0·042; p=0·654). INTERPRETATION: This cross-sectional analysis did not detect a correlation between weekend staffing of hospital specialists and mortality risk for emergency admissions. Further investigation is needed to evaluate whole-system secular change during the implementation of 7 day services. Policy makers should exercise caution before attributing the weekend effect mainly to differences in specialist staffing. FUNDING: National Institute for Health Research Health Services and Delivery Research Programme.

Statistical efficiency and optimal design for stepped cluster studies under linear mixed effects models.

In stepped cluster designs the intervention is introduced into some (or all) clusters at different times and persists until the end of the study. Instances include traditional parallel cluster designs and the more recent stepped-wedge designs. We consider the precision offered by such designs under mixed-effects models with fixed time and random subject and cluster effects (including interactions with time), and explore the optimal choice of uptake times. The results apply both to cross-sectional studies where new subjects are observed at each time-point, and longitudinal studies with repeat observations on the same subjects. The efficiency of the design is expressed in terms of a 'cluster-mean correlation' which carries information about the dependency-structure of the data, and two design coefficients which reflect the pattern of uptake-times. In cross-sectional studies the cluster-mean correlation combines information about the cluster-size and the intra-cluster correlation coefficient. A formula is given for the 'design effect' in both cross-sectional and longitudinal studies. An algorithm for optimising the choice of uptake times is described and specific results obtained for the best balanced stepped designs. In large studies we show that the best design is a hybrid mixture of parallel and stepped-wedge components, with the proportion of stepped wedge clusters equal to the cluster-mean correlation. The impact of prior uncertainty in the cluster-mean correlation is considered by simulation. Some specific hybrid designs are proposed for consideration when the cluster-mean correlation cannot be reliably estimated, using a minimax principle to ensure acceptable performance across the whole range of unknown values. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Stepped-wedge cluster randomised controlled trials: a generic framework including parallel and multiple-level designs.

Stepped-wedge cluster randomised trials (SW-CRTs) are being used with increasing frequency in health service evaluation. Conventionally, these studies are cross-sectional in design with equally spaced steps, with an equal number of clusters randomised at each step and data collected at each and every step. Here we introduce several variations on this design and consider implications for power. One modification we consider is the incomplete cross-sectional SW-CRT, where the number of clusters varies at each step or where at some steps, for example, implementation or transition periods, data are not collected. We show that the parallel CRT with staggered but balanced randomisation can be considered a special case of the incomplete SW-CRT. As too can the parallel CRT with baseline measures. And we extend these designs to allow for multiple layers of clustering, for example, wards within a hospital. Building on results for complete designs, power and detectable difference are derived using a Wald test and obtaining the variance-covariance matrix of the treatment effect assuming a generalised linear mixed model. These variations are illustrated by several real examples. We recommend that whilst the impact of transition periods on power is likely to be small, where they are a feature of the design they should be incorporated. We also show examples in which the power of a SW-CRT increases as the intra-cluster correlation (ICC) increases and demonstrate that the impact of the ICC is likely to be smaller in a SW-CRT compared with a parallel CRT, especially where there are multiple levels of clustering. Finally, through this unified framework, the efficiency of the SW-CRT and the parallel CRT can be compared.

HEADROOM APPROACH TO DEVICE DEVELOPMENT: CURRENT AND FUTURE DIRECTIONS.

OBJECTIVES: The headroom approach to medical device development relies on the estimation of a value-based price ceiling at different stages of the development cycle. Such price-ceilings delineate the commercial opportunities for new products in many healthcare systems. We apply a simple model to obtain critical business information as the product proceeds along a development pathway, and indicate some future directions for the development of the approach. METHODS: Health economic modelling in the supply-side development cycle for new products. RESULTS: The headroom can be used: initially as a 'reality check' on the viability of the device in the healthcare market; to support product development decisions using a real options approach; and to contribute to a pricing policy which respects uncertainties in the reimbursement outlook. CONCLUSIONS: The headroom provides a unifying thread for business decisions along the development cycle for a new product. Over the course of the cycle attitudes to uncertainty will evolve, based on the timing and manner in which new information accrues. Within this framework the developmental value of new information can justify the costs of clinical trials and other evidence-gathering activities. Headroom can function as a simple shared tool to parties in commercial negotiations around individual products or groups of products. The development of similar approaches in other contexts holds promise for more rational planning of service provision.

Protocol for evaluation of the cost-effectiveness of ePrescribing systems and candidate prototype for other related health information technologies.

BACKGROUND: This protocol concerns the assessment of cost-effectiveness of hospital health information technology (HIT) in four hospitals. Two of these hospitals are acquiring ePrescribing systems incorporating extensive decision support, while the other two will implement systems incorporating more basic clinical algorithms. Implementation of an ePrescribing system will have diffuse effects over myriad clinical processes, so the protocol has to deal with a large amount of information collected at various 'levels' across the system. METHODS/DESIGN: The method we propose is use of Bayesian ideas as a philosophical guide.Assessment of cost-effectiveness requires a number of parameters in order to measure incremental cost utility or benefit - the effectiveness of the intervention in reducing frequency of preventable adverse events; utilities for these adverse events; costs of HIT systems; and cost consequences of adverse events averted. There is no single end-point that adequately and unproblematically captures the effectiveness of the intervention; we therefore plan to observe changes in error rates and adverse events in four error categories (death, permanent disability, moderate disability, minimal effect). For each category we will elicit and pool subjective probability densities from experts for reductions in adverse events, resulting from deployment of the intervention in a hospital with extensive decision support. The experts will have been briefed with quantitative and qualitative data from the study and external data sources prior to elicitation. Following this, there will be a process of deliberative dialogues so that experts can "re-calibrate" their subjective probability estimates. The consolidated densities assembled from the repeat elicitation exercise will then be used to populate a health economic model, along with salient utilities. The credible limits from these densities can define thresholds for sensitivity analyses. DISCUSSION: The protocol we present here was designed for evaluation of ePrescribing systems. However, the methodology we propose could be used whenever research cannot provide a direct and unbiased measure of comparative effectiveness.

Effects of physical activity on the development and progression of microvascular complications in type 1 diabetes: retrospective analysis of the DCCT study.

BACKGROUND: To examine the effects of physical activity on the development and progression of microvascular complications in patients with type 1 diabetes. METHODS: A retrospective analysis of data from the Diabetes Control and Complications trial was undertaken. Physical activity data was collected at baseline for each of 1441 recruits, converted to metabolic equivalent of task values, and categorised according to the American College of Sports Medicine recommendations. The rates of development/progression of diabetic retinopathy, nephropathy and neuropathy were compared in those who achieved over twice recommended, up to twice recommended, and less than recommended metabolic equivalent of task levels of activity. The DCCT study had a mean duration of follow up of 6.5 years ending in 1993. RESULTS: A total of 271 subjects had a sustained three-step progression in diabetic retinopathy. The rates of development or progression of retinopathy showed no significant association with physical activity level. The number of outcomes for nephropathy and neuropathy were small and there was no significant association with physical activity level. CONCLUSIONS: We found no evidence that physical activity improves microvascular outcomes in type 1 diabetes. However we demonstrate no evidence of harm. We suggest that physical activity continues to play an important role in the management of type 1 diabetes.

Optimal study designs for cluster randomised trials: An overview of methods and results.

There are multiple possible cluster randomised trial designs that vary in when the clusters cross between control and intervention states, when observations are made within clusters, and how many observations are made at each time point. Identifying the most efficient study design is complex though, owing to the correlation between observations within clusters and over time. In this article, we present a review of statistical and computational methods for identifying optimal cluster randomised trial designs. We also adapt methods from the experimental design literature for experimental designs with correlated observations to the cluster trial context. We identify three broad classes of methods: using exact formulae for the treatment effect estimator variance for specific models to derive algorithms or weights for cluster sequences; generalised methods for estimating weights for experimental units; and, combinatorial optimisation algorithms to select an optimal subset of experimental units. We also discuss methods for rounding experimental weights, extensions to non-Gaussian models, and robust optimality. We present results from multiple cluster trial examples that compare the different methods, including determination of the optimal allocation of clusters across a set of cluster sequences and selecting the optimal number of single observations to make in each cluster-period for both Gaussian and non-Gaussian models, and including exchangeable and exponential decay covariance structures.

Pricing of medical devices under coverage uncertainty--a modelling approach.

Product vendors and manufacturers are increasingly aware that purchasers of health care will fund new clinical treatments only if they are perceived to deliver value-for-money. This influences companies' internal commercial decisions, including the price they set for their products. Other things being equal, there is a price threshold, which is the maximum price at which the device will be funded and which, if its value were known, would play a central role in price determination. This paper examines the problem of pricing a medical device from the vendor's point of view in the presence of uncertainty about what the price threshold will be. A formal solution is obtained by maximising the expected value of the net revenue function, assuming a Bayesian prior distribution for the price threshold. A least admissible price is identified. The model can also be used as a tool for analysing proposed pricing policies when no formal prior specification of uncertainty is available.

Bayesian meta-analysis on medical devices: application to implantable cardioverter defibrillators.

OBJECTIVES: The aim of this study is to describe and illustrate a method to obtain early estimates of the effectiveness of a new version of a medical device. METHODS: In the absence of empirical data, expert opinion may be elicited on the expected difference between the conventional and modified devices. Bayesian Mixed Treatment Comparison (MTC) meta-analysis can then be used to combine this expert opinion with existing trial data on earlier versions of the device. We illustrate this approach for a new four-pole implantable cardioverter defibrillator (ICD) compared with conventional ICDs, Class III anti-arrhythmic drugs, and conventional drug therapy for the prevention of sudden cardiac death in high risk patients. Existing RCTs were identified from a published systematic review, and we elicited opinion on the difference between four-pole and conventional ICDs from experts recruited at a cardiology conference. RESULTS: Twelve randomized controlled trials were identified. Seven experts provided valid probability distributions for the new ICDs compared with current devices. The MTC model resulted in estimated relative risks of mortality of 0.74 (0.60-0.89) (predictive relative risk [RR] = 0.77 [0.41-1.26]) and 0.83 (0.70-0.97) (predictive RR = 0.84 [0.55-1.22]) with the new ICD therapy compared to Class III anti-arrhythmic drug therapy and conventional drug therapy, respectively. These results showed negligible differences from the preliminary results for the existing ICDs. CONCLUSIONS: The proposed method incorporating expert opinion to adjust for a modification made to an existing device may play a useful role in assisting decision makers to make early informed judgments on the effectiveness of frequently modified healthcare technologies.

Sample size calculations for cluster randomised controlled trials with a fixed number of clusters.

BACKGROUND: Cluster randomised controlled trials (CRCTs) are frequently used in health service evaluation. Assuming an average cluster size, required sample sizes are readily computed for both binary and continuous outcomes, by estimating a design effect or inflation factor. However, where the number of clusters are fixed in advance, but where it is possible to increase the number of individuals within each cluster, as is frequently the case in health service evaluation, sample size formulae have been less well studied. METHODS: We systematically outline sample size formulae (including required number of randomisation units, detectable difference and power) for CRCTs with a fixed number of clusters, to provide a concise summary for both binary and continuous outcomes. Extensions to the case of unequal cluster sizes are provided. RESULTS: For trials with a fixed number of equal sized clusters (k), the trial will be feasible provided the number of clusters is greater than the product of the number of individuals required under individual randomisation (nI) and the estimated intra-cluster correlation (ρ). So, a simple rule is that the number of clusters (k) will be sufficient provided: [formula in text]. Where this is not the case, investigators can determine the maximum available power to detect the pre-specified difference, or the minimum detectable difference under the pre-specified value for power. CONCLUSIONS: Designing a CRCT with a fixed number of clusters might mean that the study will not be feasible, leading to the notion of a minimum detectable difference (or a maximum achievable power), irrespective of how many individuals are included within each cluster.

Should patients with abnormal liver function tests in primary care be tested for chronic viral hepatitis: cost minimisation analysis based on a comprehensively tested cohort.

BACKGROUND: Liver function tests (LFTs) are ordered in large numbers in primary care, and the Birmingham and Lambeth Liver Evaluation Testing Strategies (BALLETS) study was set up to assess their usefulness in patients with no pre-existing or self-evident liver disease. All patients were tested for chronic viral hepatitis thereby providing an opportunity to compare various strategies for detection of this serious treatable disease. METHODS: This study uses data from the BALLETS cohort to compare various testing strategies for viral hepatitis in patients who had received an abnormal LFT result. The aim was to inform a strategy for identification of patients with chronic viral hepatitis. We used a cost-minimisation analysis to define a base case and then calculated the incremental cost per case detected to inform a strategy that could guide testing for chronic viral hepatitis. RESULTS: Of the 1,236 study patients with an abnormal LFT, 13 had chronic viral hepatitis (nine hepatitis B and four hepatitis C). The strategy advocated by the current guidelines (repeating the LFT with a view to testing for specific disease if it remained abnormal) was less efficient (more expensive per case detected) than a simple policy of testing all patients for viral hepatitis without repeating LFTs. A more selective strategy of viral testing all patients for viral hepatitis if they were born in countries where viral hepatitis was prevalent provided high efficiency with little loss of sensitivity. A notably high alanine aminotransferase (ALT) level (greater than twice the upper limit of normal) on the initial ALT test had high predictive value, but was insensitive, missing half the cases of viral infection. CONCLUSIONS: Based on this analysis and on widely accepted clinical principles, a "fast and frugal" heuristic was produced to guide general practitioners with respect to diagnosing cases of viral hepatitis in asymptomatic patients with abnormal LFTs. It recommends testing all patients where a clear clinical indication of infection is present (e.g. evidence of intravenous drug use), followed by testing all patients who originated from countries where viral hepatitis is prevalent, and finally testing those who have a notably raised ALT level (more than twice the upper limit of normal). Patients not picked up by this efficient algorithm had a risk of chronic viral hepatitis that is lower than the general population.

24 hour consultant obstetrician presence on the labour ward and intrapartum outcomes in a large unit in England: A time series analysis.

OBJECTIVES: To explore the effect of introducing 24/7 resident labour ward consultant presence on neonatal and maternal outcomes in a large obstetric unit in England. DESIGN: Retrospective time sequence analysis of routinely collected data. SETTING: Obstetric unit of large teaching hospital in England. PARTICIPANTS: Women and babies delivered between1 July 2011 and 30 June 2017. Births <24 weeks gestation or by planned caesarean section were excluded. MAIN OUTCOME MEASURES: The primary composite outcome comprised intrapartum stillbirth, neonatal death, babies requiring therapeutic hypothermia, or admission to neonatal intensive care within three hours of birth. Secondary outcomes included markers of neonatal and maternal morbidity. Planned subgroup analyses investigated gestation (<34 weeks; 34-36 weeks; ≥37 weeks) and time of day. RESULTS: 17324 babies delivered before and 16110 after 24/7 consultant presence. The prevalence of the primary outcome increased by 0.65%, from 2.07% (359/17324) before 24/7 consultant presence to 2.72% (438/16110, P < 0.001) after 24/7 consultant presence which was consistent with an upward trend over time already well established before 24/7 consultant presence began (OR 1.09 p.a.; CI 1.04 to 1.13). Overall, there was no change in this trend associated with the transition to 24/7. However, in babies born ≥37 weeks gestation, the upward trend was reversed after implementation of 24/7 (OR 0.67 p.a.; CI 0.49 to 0.93; P = 0.017). No substantial differences were shown in other outcomes or subgroups. CONCLUSIONS: Overall, resident consultant obstetrician presence 24/7 on labour ward was not associated with a change in a pre-existing trend of increasing adverse infant outcomes. However, 24/7 presence was associated with a reversal in increasing adverse outcomes for term babies.

Methodological issues in economic evaluations of emergency transport systems in low-income and middle-income countries.

A recent systematic review identified few papers on the economic evaluation of systems for emergency transport of acutely ill or injured patients. In addition, we found no articles dealing with the methodological challenges posed by such studies in low-income or middle-income countries. We therefore carried out an analysis of issues that are of particular salience to this important topic. This is an intellectual study in which we develop models, identify their limitations, suggest potential extensions to the models and discuss priorities for empirical studies to populate models. First, we develop a general model to calculate changes in survival contingent on the reduced time to treatment that an emergency transport system is designed to achieve. Second, we develop a model to estimate transfer times over an area that will be served by a proposed transfer system. Third, we discuss difficulties in obtaining parameters with which to populate the models. Fourth, we discuss costs, both direct and indirect, of an emergency transfer service. Fifth, we discuss the issue that outcomes other than survival should be considered and that the effects of a service are a weighted sum over all the conditions and severities for which the service caters. Lastly, based on the above work, we identify priorities for research. To our knowledge, this is the first study to identify and frame issues in the health economics of acute transfer systems and to develop models to calculate survival rates from basic parameters, such as time delay/survival relationships, that vary by intervention type and context.

Changes in weekend and weekday care quality of emergency medical admissions to 20 hospitals in England during implementation of the 7-day services national health policy.

BACKGROUND: In 2013, the English National Health Service launched the policy of 7-day services to improve care quality and outcomes for weekend emergency admissions. AIMS: To determine whether the quality of care of emergency medical admissions is worse at weekends, and whether this has changed during implementation of 7-day services. METHODS: Using data from 20 acute hospital Trusts in England, we performed randomly selected structured case record reviews of patients admitted to hospital as emergencies at weekends and on weekdays between financial years 2012-2013 and 2016-2017. Senior doctor ('specialist') involvement was determined from annual point prevalence surveys. The primary outcome was the rate of clinical errors. Secondary outcomes included error-related adverse event rates, global quality of care and four indicators of good practice. RESULTS: Seventy-nine clinical reviewers reviewed 4000 admissions, 800 in duplicate. Errors, adverse events and care quality were not significantly different between weekend and weekday admissions, but all improved significantly between epochs, particularly errors most likely influenced by doctors (clinical assessment, diagnosis, treatment, prescribing and communication): error rate OR 0.78; 95% CI 0.70 to 0.87; adverse event OR 0.48, 95% CI 0.33 to 0.69; care quality OR 0.78, 95% CI 0.70 to 0.87; all adjusted for age, sex and ethnicity. Postadmission in-hospital care processes improved between epochs and were better for weekend admissions (vital signs with National Early Warning Score and timely specialist review). Preadmission processes in the community were suboptimal at weekends and deteriorated between epochs (fewer family doctor referrals, more patients with chronic disease or palliative care designation). CONCLUSIONS AND IMPLICATIONS: Hospital care quality of emergency medical admissions is not worse at weekends and has improved during implementation of the 7-day services policy. Causal pathways for the weekend effect may extend into the prehospital setting.