tetano
Editor, Senior Moderator
Public Health. 2015 Mar 4. pii: S0033-3506(15)00044-X. doi: 10.1016/j.puhe.2015.01.025. [Epub ahead of print]
[h=1]Forecasts of health care utilization related to pandemic A(H1N1)2009 influenza in the Nord-Pas-de-Calais region, France.[/h] Giovannelli J[SUP]1[/SUP], Loury P[SUP]2[/SUP], Lain? M[SUP]3[/SUP], Spaccaferri G[SUP]3[/SUP], Hubert B[SUP]2[/SUP], Chaud P[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]OBJECTIVES:[/h] To describe and evaluate the forecasts of the load that pandemic A(H1N1)[SUB]2009[/SUB] influenza would have on the general practitioners (GP) and hospital care systems, especially during its peak, in the Nord-Pas-de-Calais (NPDC) region, France.
[h=4]STUDY DESIGN:[/h] Modelling study.
[h=4]METHODS:[/h] The epidemic curve was modelled using an assumption of normal distribution of cases. The values for the forecast parameters were estimated from a literature review of observed data from the Southern hemisphere and French Overseas Territories, where the pandemic had already occurred. Two scenarios were considered, one realistic, the other pessimistic, enabling the authors to evaluate the 'reasonable worst case'. Forecasts were then assessed by comparing them with observed data in the NPDC region - of 4 million people.
[h=4]RESULTS:[/h] The realistic scenarios forecasts estimated 300,000 cases, 1500 hospitalizations, 225 intensive care units (ICU) admissions for the pandemic wave; 115 hospital beds and 45 ICU beds would be required per day during the peak. The pessimistic scenario's forecasts were 2-3 times higher than the realistic scenario's forecasts. Observed data were: 235,000 cases, 1585 hospitalizations, 58 ICU admissions; and a maximum of 11.6 ICU beds per day.
[h=4]CONCLUSIONS:[/h] The realistic scenario correctly estimated the temporal distribution of GP and hospitalized cases but overestimated the number of cases admitted to ICU. Obtaining more robust data for parameters estimation - particularly the rate of ICU admission among the population that the authors recommend to use - may provide better forecasts.
Copyright ? 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
[h=4]KEYWORDS:[/h] Evaluation; Influenza-like illness; Model; Preparedness; Tool
PMID: 25747568 [PubMed - as supplied by publisher]
[h=1]Forecasts of health care utilization related to pandemic A(H1N1)2009 influenza in the Nord-Pas-de-Calais region, France.[/h] Giovannelli J[SUP]1[/SUP], Loury P[SUP]2[/SUP], Lain? M[SUP]3[/SUP], Spaccaferri G[SUP]3[/SUP], Hubert B[SUP]2[/SUP], Chaud P[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]OBJECTIVES:[/h] To describe and evaluate the forecasts of the load that pandemic A(H1N1)[SUB]2009[/SUB] influenza would have on the general practitioners (GP) and hospital care systems, especially during its peak, in the Nord-Pas-de-Calais (NPDC) region, France.
[h=4]STUDY DESIGN:[/h] Modelling study.
[h=4]METHODS:[/h] The epidemic curve was modelled using an assumption of normal distribution of cases. The values for the forecast parameters were estimated from a literature review of observed data from the Southern hemisphere and French Overseas Territories, where the pandemic had already occurred. Two scenarios were considered, one realistic, the other pessimistic, enabling the authors to evaluate the 'reasonable worst case'. Forecasts were then assessed by comparing them with observed data in the NPDC region - of 4 million people.
[h=4]RESULTS:[/h] The realistic scenarios forecasts estimated 300,000 cases, 1500 hospitalizations, 225 intensive care units (ICU) admissions for the pandemic wave; 115 hospital beds and 45 ICU beds would be required per day during the peak. The pessimistic scenario's forecasts were 2-3 times higher than the realistic scenario's forecasts. Observed data were: 235,000 cases, 1585 hospitalizations, 58 ICU admissions; and a maximum of 11.6 ICU beds per day.
[h=4]CONCLUSIONS:[/h] The realistic scenario correctly estimated the temporal distribution of GP and hospitalized cases but overestimated the number of cases admitted to ICU. Obtaining more robust data for parameters estimation - particularly the rate of ICU admission among the population that the authors recommend to use - may provide better forecasts.
Copyright ? 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
[h=4]KEYWORDS:[/h] Evaluation; Influenza-like illness; Model; Preparedness; Tool
PMID: 25747568 [PubMed - as supplied by publisher]