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Work on vaccination timing and promotion largely precedes the 2009 pandemic. Post-pandemic studies examining the wide range of local vaccination efforts mostly have been limited to surveys assessing the role of administrative strategies, logistical challenges, and perceived deterrents of vaccination [1].

Objective

To assess the effectiveness of a Public Health automated phone campaign to increase vaccination uptake in targeted neighborhoods. To identify alternative predictors of variation in vaccination uptake, specifically to assess the association between vaccination uptake, and weather conditions and day-of-week.

Referenced File
Submitted by elamb on

INDICATOR is a multi-stream open source platform for biosurveillance and outbreak detection, currently focused on Champaign County in Illinois. It has been in production since 2008 and is currently receiving data from emergency department, patient advisory nurse, outpatient convenient care clinic, school absenteeism, animal control, and weather sources. Historical data from some of these sources goes back to 2006.

 

Objective

To examine the correlation between different types of surveillance signals and climate information obtained from a well-defined geographic area.

Referenced File
Submitted by elamb on

The ability to estimate and characterize the burden of disease on a population is important for all public health events, including extreme heat events. Preparing for such events is critical to minimize the associated morbidity and mortality [1, 2]. Since there are delays in obtaining hospital discharge or death records, monitoring of ED visits is the timeliest and an inexpensive method for surveillance of HRI [1]. Aside from air temperature, other environmental variables are used to issue heat advisories based on the heat index, including humidity and wind [3]. The purpose of this study was to evaluate the relationship between HRI ED visits and weather variables as predictors, in Ohio.

Objective

Correlation and linear regression analyses were completed to evaluate the relationship between a heat-related illness (HRI) classifier using emergency department (ED) chief complaint data and specific weather variables as predictors, in Ohio.

Referenced File
Submitted by elamb on

This is a cluster of syndromes created to populate an extreme weather myESSENCE tab. The intent was to increase repeatability of our weather surveillance and have something where a user can use the "Change Region" option to select whatever county, or counties, experienced storm activity. This is still a major work-in progress.

All of this as done in NSSP ESSENCE on Emergency Room data. Fields are specified by each syndrome definition.

Submitted by ZSteinKS on

In late summer 2017, the United States endured two severe hurricanes back to back. On August 25, 2017, Hurricane Harvey made landfall in Texas and southwest Louisiana, dumping more than 19 trillion gallons of rain. On September 10, 2017, 20 days later, Hurricane Irma landed in Florida, leading residents across the Florida peninsula to evacuate inland and out of the path of the storm. Although Tennessee was far from the eye of the storms, state health officials knew residents from both states could choose to shelter in Tennessee.

Submitted by elamb on

Although effective preventive measures for heat-related illness have been recommended and mandated for military personnel, there continues to be incident cases. In 2016, there were 401 incident cases of heat stroke and 2,135 incident cases of “other heat illness” among all active component service members. Current military guidelines utilize the wet bulb globe temperature (WBGT) index to measure heat risk, guiding work/rest and hydration practices. The WBGT requires calibrated instrumentation and is based on fixed cutoff values. We propose using readily available meteorological data inputs and EHI cases to identify and validate an EHI risk prediction model. Prior studies have found that combinations of WBGT and the previous day’s WBGT and relative humidity and temperature have predictive value for EHI. We build upon prior work by using generalized additive models (GAMs).

Objective:

To identify predictors of the risk of developing exertional heat illness (EHI) among basic training populations in the Department of Defense.

Submitted by elamb on

Weather events such as a heat wave or a cold snap can cause a change to the number of patients and types of symptoms seen at a healthcare facility. Understanding the impact of weather patterns on ILI surveillance may be useful for early detection and trend analysis. In addition, weather patterns limit our ability to extrapolate data collected in one region to a different region, which may not share the same weather or periodic trends. By modeling these sources of variation, we can factor out their effects and increase the sensitivity of our overall surveillance system.

Objective

To develop a statistical model to account for weather variation in influenza-like illness (ILI) surveillance.

Submitted by teresa.hamby@d… on

On January 2, 2014 the cyclone Bejisa struck Reunion Island. This storm of Category 3 (Saffir–Simpson scale) disturbed electricity supply and drinking water systems. Floods, roof destructions and the threat of landslide led to the evacuation of residents to emergency shleters. In this context, the regional office of French Institute for Public Health Surveillance in Indian Ocean set up an epidemiological surveillance in order to assess the impact in the aftermath of the cyclone.

Objective

To assess the health impact of cyclone Bejisa from data of emergency departments (EDs) and emergency medical service (EMS)

Submitted by teresa.hamby@d… on
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