Skip to main content

Timeliness

Description

The performance of even the most advanced syndromic surveillance systems can be undermined if the monitored data is delayed before it arrives into the system.  In such cases, an outbreak may be detected only after it is too late for appropriate public health response. Surveillance systems can experience delays in data availability for a number of reasons: The process of transmitting data from data sources to the surveillance system can involve delays, especially in large systems where data is first aggregated across a national network of data sources before being transmitted to the surveillance system. Delays can also arise in the course of care, where, for example, a diagnosis is not available for a few days after the healthcare encounter.  It is important to minimize delays in data availability in order to maintain timeliness of detection [1].  When this is not possible, it is desirable to compensate for these data delays to minimize their effects.

Objective

This paper describes an approach to improving the detection timeliness of real-time health surveillance systems by modeling and correcting for delays in data availability.

Submitted by elamb on
Description

In November of 2001 a syndromic surveillance system was established in Los Angeles (LA) County to analyze emergency department (ED) chief complaints in select hospitals. Chief complaints were analyzed and categorized into a syndrome (rash, respiratory, neurological, gastrointestinal), and an algorithm was developed to create a daily threshold for each category. Questions remain as to what events can be detected by the system in a timely manner. On the community level, of interest is whether an outbreak with a wide epidemiological curve would have the intensity of case visits needed to trigger a signal. On the individual level, of interest is the length of time it takes for a person with a given disease characteristic to seek medical attention, whether medical care is sought in the ED first, and how the syndromic system classifies them upon visiting the ED. To address these questions the 2004 LA County West Nile community-wide outbreak was selected for review, with a focus on the more severe neuro-invasive cases.

 

Objective

To evaluate the effectiveness of monitoring emergency room chief complaints as an indicator for a neuro-invasive disease outbreak.

Submitted by elamb on
Description

The Centre for Health Protection in Hong Kong has operated a sentinel surveillance system for infectious diseases at child care centre (CCC) since March 2004, among its multi-faceted disease surveillance systems. Forty-six CCCs have participated in the system and are contributing data weekly on absenteeism and common infectious disease symptoms such as fever, diarrhea, vomiting, and cough. The system was originally driven by a manual data collection mechanism via fax, followed by secondary data input and subsequent analysis. However, such mechanism might sometimes result in delayed data transmission and data loss. As an alternative to accommodate these limitations, a web-based platform is developed to increase the timeliness of data submission by the sentinel CCCs. The new platform not only speeds up data collection and eliminates the need for human data entry, but at the same time delivers summary statistics directly on the web through computer programmes on a real time basis, as soon as data is entered by the provider.

 

Objective

This paper describes the attempt to develop an internet-based community surveillance network to enhance timeliness and sensitivity in detecting community-wide infectious disease outbreaks among young children at CCCs in Hong Kong.

Submitted by elamb on
Description

San Francisco has the highest rate of TB in the US. Although in recent years the incidence of TB has been declining in the San Francisco general population, it has remained relatively constant in the homeless population. Spatial investigations of disease outbreaks seek to identify and determine the significance of spatially localized disease clusters by partitioning the underlying geographic region. The level of such regional partitioning can vary depending on the available geospatial data on cases including towns, counties, zip codes, census tracts, and exact longitude-latitude coordinates. It has been shown for syndromic surveillance data that when exact patients’ geographic coordinates are used, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. While the benefits of using a finer spatial resolution, such as patients’ individual addresses, have been examined in the context of spatial epidemiology, the effect of varying spatial resolution on detection timeliness and the amount of historical data needed have not been investigated.

 

Objective

The objective of this study is to investigate the effect of varying the spatial resolution in a variant of space-time permutation scan statistic applied to the tuberculosis data on the San Francisco homeless population on detection sensitivity, timeliness, and the amount of historical data needed for training the model.

Submitted by elamb on
Description

New York City ED syndromic surveillance data uses SaTScan to detect spatial signals. SaTScan analysis has been integrated into SAS since 2002, and signal maps have been generated from SAS since 2003. Signal maps are created occasionally to investigate a severe outbreak based on the SaTScan results. Previous use and integration of additional GIS analysis in ArcGIS has been done manually, requiring more time, and running the risk of being less consistent than an automated method. This script now integrates the SAS, SaTScan and spatial analysis from ArcGIS to create high-quality maps in an automated procedure.

 

Objective

The objective was to minimize the amount of time spent on routine, daily analysis of syndromic data, integrate additional spatial analysis, create better maps, and cut response times to outbreaks.

Submitted by elamb on
Description

Previous studies have documented significant lags in official reporting of outbreaks compared to unofficial reporting (1,2). MoH+ provides an additional tool to analyze this issue, with the unique advantage of actively gathering a wide range of streamlined official communication, including formal publications, online press releases, and social media updates.

Objective:

To introduce MoH+, HealthMap’s (HM) real-time feed of official government sources, and demonstrate its utility in comparing the timeliness of outbreak reporting between official and unofficial sources.

 

Submitted by Magou on
Description

Temporal alerting algorithms commonly used in syndromic surveillance systems are often adjusted for data features such as cyclic behavior but are subject to overfitting or misspecification errors when applied indiscriminately. In a project for the Armed Forces Health Surveillance Center to enable multivariate decision support, we obtained 4.5 years of outpatient, prescription and laboratory test records from all US military treatment facilities. A proof-of-concept project phase produced 16 events with multiple evidence corroboration for comparison of alerting algorithms for detection performance. We used the representative streams from each data source to compare sensitivity of 6 algorithms to injected spikes, and we used all data streams from 16 known events to compare them for detection timeliness.

Objective

For a multi-source decision support application, we sought to match univariate alerting algorithms to surveillance data types to optimize detection performance.

Submitted by uysz on
Description

Swaziland adopted the Integrated Disease Surveillance and Response (IDSR) strategy in 2010 to strengthen Public Health Surveillance (PHS) that fulfills International Health Regulations (2005) and the Global Health Security Agenda (GHSA). This strategy allows the Ministry of Health (MoH), Epidemiology and Disease Control Unit (EDCU) to monitor, prevent and control priority diseases in the country. We used a health systems strengthening approach to pilot an intervention model for IDSR implementation at five hospitals in Swaziland over a pilot phase of three months.

Objective:

To strengthen public health surveillance and monitor implementation of Integrated Disease Surveillance and Response in the Kingdom of Swaziland.

Submitted by elamb on
Description

Today, surveyors in both the private and public sectors are facing considerable challenges with random digit dialed (RDD) landline telephone samples. The population coverage rates for landline telephone surveys are being eroded by wireless-only households, portable telephone numbers, telecommunication barriers (e.g., call forwarding, call blocking and pager connections), technological barriers (call-blocking, busy circuits) and increased refusal rates and privacy concerns. Addressing these issues increasingly drives up the costs associated with dual-frame telephone surveys designed to be representative of the target population as well as hinders their ability to be fully representative of the adult population of each state and territory in the United States. In an effort to continue to meet these challenges head on and assist state and territorial public health professionals in the continued collection of data that are representative of their respective populations, novel approaches to behavioral health surveillance need continued examination. Both private and public sector researchers are evaluating the use of Internet opt-in panels to augment dual-frame RDD survey methods. Compared to dual-frame RDD, opt-in Internet panels offer lower costs, quick data collection and dissemination, and the ability to gather additional data on panelists over time. However, as with dual-frame RDD, this mode has similar challenges with coverage error and non-response. Nevertheless, survey methodologists are moving forward and exploring ways to reduce or eliminate biases between the sample and the target population.

Objective

To present the design and preliminary results of a pilot study to investigate the use of opt-in Internet panel surveys for behavioral health surveillance.

 

Submitted by Magou on
Description

Timeliness of emergency room (ER) data is arguably its strongest attribute in terms of its contribution to disease surveillance. Timely data analyses may improve the efficacy of prevention and control measures. There are a number of studies that have looked at timeliness prior to the advent of Meaningful Use, and these studies note that ER data were not fast enough for them to be useful in real time2,3. However, the change in messaging practices in the Meaningful Use era potentially changes this. Other studies have shown that changes in processes and protocol can dramatically improve timeliness1,4 and this motivates the current study of timeliness to identify processes that can be changed to improve timeliness.

Objective:

To explore the timeliness of emergency room surveillance data after the advent of federal Meaningful Use initiatives and determine potential areas for improvement.

Submitted by elamb on