Page 197 - Timeliness of Infectious Disease Notification & Response Systems - Corien Swaan

Notification timeframes according literature
Chapter 3 describes the outcomes of a systematic literature review on evalua- tions of timeliness of notifications systems published between 2000 and 2017. We used the notification chain as framework to compare outcomes between studies (Figure 1, this thesis). Notification delay D3, either by physicians or lab- oratories or combined, was studied most frequently (33/48 studies), focusing on the ‘administrative’ part of notification. We considered timeframes used by the studies ‘predefined’ timeframes, which, in some studies, derived from legis- lation (‘mandatory notification’). Otherwise, authors provided little background explanation on how the predefined timeframes were chosen, and timeframes were usually not disease specific. Exceptions were timeframes determined by the period for effective post exposure prophylaxes for contacts of hepatitis A cases, and timeframes based upon incubation periods, used as a surrogate for period of communicability that is critical to take into account when implement- ing effective, disease-specific prevention and control measures (13-16).
Notification timeframe for outbreak control
Regarding the total local reporting delay (D1), we determined in Chapter 2 that in the Netherlands, even with a hypothetical notification delay of 0 days, a substantial percentage of three out of six investigated diseases were not no- tified to the MHS within one or two incubation periods after the disease on- set of the index. This has been observed in other studies as well (13, 16). This means that at the time of notification, depending on period of infectiousness of a specific disease, the index, and even secondary cases, already might be infectious. The delay is too long for the MHS to prevent the index and direct contacts from transmitting the disease. We concluded that the disease identi- fication time takes so long that even with optimal reduced notification delays, total local reporting delays will not be timely to prevent transmission from index and contacts. Therefore another additional timeframe is necessary, which takes the total reporting delay on local level, including disease identification time, into account. We developed a model to determine disease specific timeliness of total local reporting delay (D1) during which the MHS is able to install timely outbreak control measures. Chapter 4 describes the model, which quantifies reporting timeliness for outbreak control for six person-to-person transmissible diseases, by calculating the proportion of secondary cases caused by the index and by secondary cases at the time the index case is notified at the MHS. The model uses the disease specific serial interval distribution, the distribution of the incubation period and the range of the reproduction number, to determine
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