In our calculations, we also factored the effect of treatment (within 48 hrs of infection) with antiviral medication on the spread and the impact of the pandemic, although the exact effect size is still uncertain, specifically for children (16, 30). Antiviral medication is assumed to reduce the total number of hospital admissions by 50% and death rate by ≈30% (29).
In addition, we incorporated in the model the probable absenteeism of HCWs either due to illness or to care duties at home or in individual social environments. We assumed that HCWs will fall ill at a rate similar to that of the general population. We extrapolated national population data of illness and deaths to the total number of HCWs in our HCW database.
We present the impact of a pandemic on children with new human-to-human-transmissible influenza on hospital and PICU resources in the UMCG. Using the figures of the RIVM, and assuming a 30% cumulative disease attack rate, we estimated that approximately 12% of the population of children will consult a general practitioner (Table 2A–C). The percentage of children triaged for hospital admission is 0.3%. We assumed excess deaths among these selected patients, some 25% or 50% of whom may require mechanical ventilation, for age groups below 18 yrs (Fig. 1), age group <7 yrs (Fig. 2), and age groups >18 yrs (Fig. 3). In the northern part of The Netherlands, 5629 regular hospital beds are available. The hospitals in this region have a total of 30% (noninfluenza-related) acute care, which would leave 3940 regular hospital beds that could be made available for influenza-related hospital admissions. In addition, the UMCG has 100 pediatric hospitals beds, of which 70 could be made available for influenza-related hospital admissions. If the attack rate reaches a maximum of 50% with a mean length of stay of 15 hospital days per child, without any additional change in patient management, this would lead to a peak of 282 occupied regular pediatric hospital beds in the age group <18 yrs, which would suffice for influenza-related acute care. Therefore, we centered our calculations around the peak occupancy of PICU beds. We calculated the number of hospital admissions per week, spread evenly across 7 days in the respective week, and we subtracted the number of deaths, also evenly spread across the week. We assumed that 25% to 50% of total hospital admission pediatric patients would require some form of mechanical ventilatory support, and we provide calculations for the extremes of our estimates. On the basis of results from a semistructured telephone interview with ICU medical staff in the UMCG, a maximum of 31 (total n = 46) PICU beds and a maximum of 136 adult ICU beds (total n = 200) could be used for influenza-related acute care patients. Furthermore, a total of approximately 70 (total n = 100) operating theater beds with mechanical ventilators can be available for a short period of time, allowing 30 operating theater beds for acute postoperative care. We estimate that 31 PICU beds and 136 adult ICU beds will be made available in a short period. In the scenario of no additional intervention, if the full capacity of all 31 PICU beds is used, with an attack rate of 30%, 25% ICU admission rate, and a mean length of stay of 8 days, we would have a shortage of one PICU bed at day 28 after onset, when we expect the pandemic to peak. This shortage of ICU capacity is exacerbated with any increase in hospital length of stay or ICU length of stay. As HCWs will become ill in the pandemic in proportion to the attack rate in the general population, we illustrated the impact of HCW absenteeism on loss of ICU bed capacity for all presented scenarios (Figs. 1–3).
Finally, we made sensitivity analyses, with changing assumptions within the model. This additional material is presented in the Appendix.
We provide calculations for pediatric hospital beds and PICU capacity for an influenza pandemic for one region in The Netherlands, showing that even during the peak of the pandemic, hospital facilities can continue to provide adequate healthcare service to the public. As a novel element, we include calculations for HCW absenteeism. We have not considered potential erosion of professionalism with increased absenteeism due to fear and panic among staff or due to staff members' caring for sick family members. Although morale was high during the severe acute respiratory syndrome outbreak in Singapore and Toronto (31), some examples of strained professional behavior have been reported (32). We believe that erosion of professionalism and morale may be partly preventable by implementing effective protection for HCWs (33, 34), with appropriate training to comply with protocols for personal protection. In our hospital protocol for management of patients of new pandemic influenza and of other high-risk respiratory pathogens, we have included extensive measures to separate these patients from other patients and focus on the protection of staff (1). Adherence to similar protocols has been shown to protect HCWs caring for patients with severe acute respiratory syndrome (34).
For a new pandemic, the important issues to factor in are magnitude and duration, calculation of staff shortages, and the limited capacity to call in external resources.
We show that an influenza pandemic for children can be managed, even allowing emergency care for noninfluenza-related acute cases, especially when firm decision making rules are followed and antiviral therapy is used. With appropriate patient management, adequate health care can be provided even during the peak of the pandemic. We recognize the impact this may have on the pediatric clinicians and nurses who must make these decisions. Many clinicians now realize that end-of-life decisions are an integral part of health care (35–37) and can be considered independent of any specific religious background or culture (38). ICU staff in The Netherlands have been trained to take charge of decision processes about forgoing life support in the ICU (35). They are aware of potential difficulties in communicating with members of the ICU team, including medical, nursing, and technical staff in decisions at the end of life. The challenge during an outbreak of pandemic influenza will be in orchestrating and implementing these decisions under extreme time pressure. Relatives of patients as well as team members may need more time than is available to accept that some patients on life support who are not responding to treatment will not recover. Some may insist on continuation of support, although it would be unwise and possibly disrespectful to these patients to continue futile treatment and unfair to others who might have been saved if those resources had been available. A generous and time-consuming approach may not apply under the anticipated extreme conditions of pandemic influenza. Decision making rules have to be adapted to real-time information updates obtained during the course of the pandemic, and briefings and exchange of information throughout the pandemic crisis are pivotal. Our overall assessment that an influenza pandemic with assumptions described here can be managed at the level of healthcare institutions clearly contrasts with the sobering and daunting analysis presented for ICU capacity in the United Kingdom or Australasia (16, 21) and relates more closely to the developments observed in the evolving new influenza H1N1(v) (39–42).
There are several limitations to our analysis. We based our model on incomplete and sometimes conflicting or inconsistent information on the impact of an influenza pandemic. We assume that more reliable data will only become available when the pandemic is in progress. The effect of antiviral medications, vaccination campaigns, and, for instance, closure of schools and airports may alter the key characteristics of the pandemic, all having the effect that onset is delayed and that the course is more protracted, with a much lower peak (14). Even a less-than-perfect vaccine might have a tremendous impact on the course of the pandemic. The need for surge capacity of hospital resources is more dependent on the combination of excess hospital admissions and length of stay than on the mere number of hospital admissions. The small percentage of children admitted to the hospital in our model (based on past experiences) implies that relatively small increases in admission rate will have a huge impact on hospital resources requirement. Another limitation to our study is using a cutoff point at 25 kg for children eligible for admission to an adult ICU. We acknowledge the immense pressure this would place on staff accustomed to caring for adults. If however, as can be observed from the development of the novel influenza H1N1(v), mortality in patients admitted to ICUs is lower (as might be expected based on the 1918–1919 influenza pandemic), a huge strain on already scarce surge capacity of hospital and intensive care beds could be expected (39–46). The recommendations of the Taskforce Influenza Dutch Society for Intensive Care (NVIC) to the Minister of Health in The Netherlands are drawn up by adult intensive care physicians, focusing attention on the care of children in an adult intensive care environment (24).
We modeled pediatric surge capacity of healthcare facility and PICU requirements over time to assess the adequacy of preparedness planning for an influenza pandemic. PICU facilities will be a major limiting factor in healthcare provision for children but detailed analyses for needs and demands in PICU care have not been published. We show that PICU surge capacity is likely to be adequate, assuming that “older children” (age, >7–8 yrs) can be rerouted to an adult ICU environment preserving adequate bed space for “younger children,” that enough adult ICU resources are available, and that safe provision of care can be guaranteed. To this end, PICU physicians and nurses need to be prepared and trained to assist in order that care be delivered in adult surgical, medical, and mixed ICUs. PICU physicians and nusrses must also familiarize themselves with the adult ICU setting and environment; similarly, adult ICU physicians and nurses, as well as anesthesiologists, need to be prepared and trained to assist in order for care to be delivered in PICU environments.
We recommend that a model, with assumptions that can be adapted with new information obtained during early stages of an evolving pandemic, be an integral part of a preparedness plan for a pandemic influenza with new human transmissible agent like influenza A virus.
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APPENDIX – PANDEMIC INFLUENZA and PEDIATRIC INTENSIVE CARE
This appendix includes sensitivity analysis. Models for estimating healthcare demands, incidence, and prevalence in different scenarios and after intervention strategies. In the models, the following assumptions were made:
- Attack rates of 25%, 30%, and 50%.
- The age-specific attack and complication rates are as they would be in a normal influenza epidemic.
- Healthcare, including use of antimicrobial agents, would be equal to that of a normal influenza epidemic.
- Therapeutic use of one treatment of neuraminidase inhibitors (administered within 48 hrs after onset of symptoms) gives a 50% reduction in hospital admissions and proportion of deaths.
- No upper limit inhibitors shortage has been incorporated in models.
- Total high-risk group per 100,000 inhabitants is based on registrations from databases of general practitioners.
- Duration of the pandemic period is based on historical data, although local and regional differences in duration can occur.
- Basic reproductive number R0 was set at 1.4.
Formulae (adapted from Hagenaars et al  and Van Genugten et al )
TotPop = Total population divided into age and risk groups
PopatRisk = Population at risk
HCcmr Influenza-like illness = Number of general practitioner consultations per 100,000 inhabitants
ZHObaltussen = Number of hospital admissions per 100,000 inhabitants (adapted from Baltussen (3))
Ssprenger = Proportion of deaths attributable to influenza per 100,000 inhabitants (adapted from Sprenger (4))
HCrate = General practitioners' consultation rate for influenza-like illness
ZHOrate = Hospital admission rate for influenza
Srate = Mortality rate as a result of influenza
AR_Pandemic/Normal epidemic = Pandemic attack rates versus “normal” epidemic attack rates
Formulae for Nonintervention Scenario
HCrate = HCcmr Influenza-like illness
ZHOrate = ZHObaltussen
Srate = Ssprenger
PopatRisk = TotPop
Number of general practitioner consultations = HCrate × PopatRisk ×
Number of hospital admissions = ZHOrate × PopatRisk × AR_Pandemic/Normal epidemic
Mortality = Srate × PopatRisk × AR_Pandemic/Normal epidemic
All models are based on 0.3% hospital admission rate for infected patients. Changing this rate will have a significant impact on the peak demand for hospital beds and ICU beds. The maximum number of regular hospital beds in the 15 hospitals in the 3 northern provinces of the Netherlands equals 5629 of which 3940 could be made available for influenza-related hospital admissions (30% of all admissions are for acute, non-influenza-related care). The maximum number of ICU beds that could be made available for influenza-related care equals 136. The number of pediatric hospital beds in the UMCG is 100. The maximum number of pediatric intensive care beds that could be made available for influenza-related care equals 31.
In the next tables, we present the difference (i.e., surplus or deficit) between demand and capacity for ICU beds at the peak of the pandemic for a mean length of stay of 8 and 15 days with a maximum of 31 available PICU beds for different hospital admission rates and 30% attack rate.
For example: with a 0.3% hospital admission rate, 50% ICU admission rate, and a mean length of stay of 8 days and no intervention with antiviral medication (Appendix Table 21), a shortage of 33 PICU beds will occur at the peak of the pandemic. Restricting access to the PICU beds for children up to 7 or 8 years, a large part of the children aged 8 and over can be placed at ICU beds in the adult ICUs. For a short period of time this shortage can also be bridged by utilizing any form of respiratory support available in the hospitals (e.g., operating room ventilators, medical specialists, nurses, medical students).
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