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Mathematical modelling has provided the advancement necessary to take the hypotheses of seasonality and apply them to the field. Nevertheless, given the complexity of seasonality, a serious limiting factor to quantitative analyses and predictive models of disease patterns is the lack of long-term disease records with similar data collected over a network of spatial locations.2
There are a multitude of complexities associated with running a clinical trial where a seasonal disease is the target, or could impact the trial itself. But there are strategies that can allow for the control and quality seen in less complex trial management to be transferred to the ever variable field of seasonal diseases [see sidebar].
The planning and management of timelines are key to successfully running a seasonal clinical trial. This includes from drafting the study design through document submissions and patient recruitment, to the trial itself. Teams employed must be focused, efficient, and resourceful, as well as fully trained in the indication studied and its seasonality.
This section focuses largely on the discussion of infectious diseases. Ultimately, noninfectious diseases such as seasonal allergies are much more predictable, and the principles discussed here can be easily applied. However, the challenges arise when dealing with infectious diseases where seasonality can be much more volatile and where trial management must be both robust and flexible.
Recruitment, planning, and timelines
It is common knowledge that almost 50% of delays in trials are a result of problems with subject recruitment.11 Naturally, subject recruitment for a seasonal clinical trial needs to be completed before the studied indication reaches its peak incidence (e.g., for indications with short seasons), and perhaps even before the increase in incidence begins if the study is to be carried out over the duration of the season.
This alone raises issues. If one is recruiting patients for a seasonal indication, before that indication is evident, how can a site determine whether a patient will meet the inclusion criteria? For some indications, such as seasonal allergic rhinitis, inclusion could be based upon the patient's medical records, but for an indication such as influenza there is no way of knowing who will contract the virus. In these cases, there needs to be a rapid recruitment program in place, to enroll patients as soon as the disease is diagnosed. It would then be wise to select sites with the capabilities to recruit larger numbers of patients within short time frames.
Conducting detailed feasibility for such diseases requires a thorough review of the incidence of the disease in previous seasons and an assessment of the factors that influenced its incidence (e.g., particularly low winter temperatures). It is also of utmost importance to submit the trial to additional countries, as a delay of one month in obtaining regulatory approval in a single country could potentially set the trial back by six months. The luxury of extending the patient recruitment period for this type of trial is simply not available.
Study design planning, protocol writing, site selection, and investigator training need thorough attention regarding indication
awareness, project management, and time management. In particular, the following should be noted:
With subject recruitment timing being such a key factor, it will be essential to submit clinical trial documents to the Ethics Committee (EC)/Competent Authority (CA) in good time in order to receive approval of a trial before the planned start of subject recruitment. Certainly in the northern hemisphere, EC/CA members, investigators, and patients tend to be on vacation during the summer months, and clinical trial approvals (as well as investigator contract approvals and patient recruitment) are therefore delayed over this time. Submissions must be timed in coordination with the EC/CAs, even if it means early submission in order to guarantee trial set-up timelines are met.
Mathematical models and surveillance programs
Mathematical models of infectious diseases have contributed significantly to scientific understanding of the dynamics of epidemics as they spread through large populations.12
The traditional model used is the SIR (susceptible-infected-resistant) model.13 This model comprises Susceptible individuals (S), Infected individuals (I), and Removed individuals (R). Each individual begins in the susceptible class, only to move to the infected class after coming into contact with an infected person. Infected individuals eventually recover from the disease and move on to the recovered class. Being "recovered" and unable to be infected once again, they are essentially removed from the population and play no further role in the dynamics. Epidemics are continuously fueled by the constant supply of new susceptibles that arise due to the birth of new individuals.19
Essentially the SIR model is basic and does not account for the many factors that influence seasonal diseases. More recently, these factors have been incorporated, including seasonality and "skipping dynamics," the principles of which yield promising forecasting tools,18,19 which have the potential to be used for managing seasonal trials. Mathematical models have downfalls, as nature is never predictable, even if incorporating so-called chaos factors. For one particular season, the model may be way off mark, and trial start-up and progress based on such a model would be thrown out of sync with the disease incidence.
Throughout the world there are a multitude of communicable disease surveillance programs coordinated by bodies such as the WHO, the Health Protection Agency, and the Centers for Disease Control and Prevention. Communicable disease surveillance is the continuous monitoring of the frequency and distribution of disease and death due to infections that can be transmitted from human to human or from animals, food, water or the environment to humans, and the monitoring of risk factors for those infections.
Significantly, an important part of communicable disease surveillance is to detect the occurrence of outbreaks or epidemics so that immediate action can be taken to identify and control the source (e.g., outbreaks of food poisoning) or so that the health service is prepared to deal with increased numbers of patients (e.g., in a flu epidemic).19
When managing a seasonal study, it is important to maintain good communication with the local, national, and international surveillance networks, to time study start up appropriately.
Mathematical modelling and disease surveillance are intricately linked in the monitoring and control of communicable diseases, and it would be a serious omission on the clinical trial manager's behalf if these vast resources were not utilized in the planning and conduct of a seasonal trial.
Conducting seasonal clinical trials is certainly complex, involving intricate time management and disease forecasting requirements. Full planning is paramount, and it has been seen that flexible patient recruitment, site selection, and document submission plans are necessary for a successful seasonal clinical trial.
Yamin Khan*, PhD, is executive vice president, Clinical
Development for Pharm-Olam International, email:
*To whom all correspondence should be addressed.
1. M. Lipsitch and C. Viboud, "Influenza Seasonality: Lifting the Fog," Proceedings of The National Academy of Sciences, 106 (10) 3645-3646 (March 10, 2009).
2. M. Pascual and A. Dobson, "Seasonal Patterns of Infectious Diseases," PLoS Medicine, 2 (1) e5 (January 2005).
3. FluNet, the World Health Organisation Communicable Disease Global Atlas, http://gamapserver.who.int/GlobalAtlas/DataQuery/viewData.asp|~http://gamapserver.who.int/GlobalAtlas/DataQuery/viewData.asp .
4. D.P. Skoner, "Allergic Rhinitis: Definition, Epidemiology, Pathophysiology, Detection, and Diagnosis," Journal of Allergy and Clinical Immunology, 108 (1) S2-S8 (July 2001).
5. A.S. Kemp, "Allergic Rhinitis," Paediatric Respiratory Reviews, 10 (2) 63-68 (June 2009).
6. L.N. Rosen, S.D. Targum, M. Terman et al., "Prevalence of Seasonal Affective Disorder at Four Latitudes," Psychiatry Research, 31 (2) 131-144 (1990).
7. S.A. Morrissey, P.T. Raggatt, B. James et al., "Seasonal Affective Disorder: Some Epidemiological Findings from a Tropical Climate," Australia and New Zealand Journal of Psychiatry, 30 (5) 579-586 (1996).
8. N. Iikuni, A. Nakajima, E. Inoue et al., "What's in Season for Rheumatoid Arthritis Patients? Seasonal Fluctuations in Disease Activity," Rheumatology, 46 (5) 846-848 (2007).
9. H. Aikman, "The Association Between Arthritis and the Weather," International Journal of Biometeorology, 40 (4) 192-199 (1997).
10. A. Fleming, J.M. Crown, M. Corbett, "Early Rheumatoid Disease," Annals of Rheumatic Disease, 35, 357-360 (1976).
11. R.M. Anderson and R.M. May, Infectious Diseases of Humans: Dynamics and Control (Oxford University Press, New York, 1991).
12. L. Stone, R. Olinky, A. Huppert, "Seasonal Dynamics of Recurrent Epidemics," Nature, 446 (7135) 533-536 (2007).
13. Health Protection Agency, "Surveillance," http://www.hpa.org.uk/webw/HPAweb&Page&HPAwebAutoListName/Page/1158313434400?p=1158313434400|~http://www.hpa.org.uk/webw/HPAweb&Page&HPAwebAutoListName/Page/1158313434400?p=1158313434400 .