GIS

The department of Paediatrics is conducting a HDSS with focus on maternal and child health at peri - urban sites located in Karachi, Pakistan. The study catchment area is 19 sq km with a population of around 274,856. Females between 15 to 49 years of age and less than 5 years old children cohort is around 67,802 and 39,028 respectively. In 2012 around 12557 pregnant women (PW) and 9,136 newborns (NB) were followed through active surveillance. As part of e-mapping the study catchment area which consists of around 50,520 structures has been digitized.

Objective

Geospatial reports are generated to facilitate an ongoing health demographic surveillance system (HDSS) conducted at a peri urban site of Karachi Pakistan. The geospatial maps facilitate in monitoring and protocol adherence of HDSS. In addition different geospatial relationships can be analyzed and various epidemiological patterns can be studied.

The New York City Department of Health and Mental Hygiene (NYC DOHMH) collects data daily from 50 of 61 (82%) emergency departments (EDs) in NYC representing 94% of all ED visits (avg daily visits ~10,000). The information collected includes the date and time of visit, age, sex, home zip code and chief complaint of each patient. Observations are assigned to syndromes based on the chief complaint field and are analyzed using SaTScan to identify statistically significant clusters of syndromes at the zip code and hospital level. SaTScan employs a circular spatial scan statistic and clusters that are not circular in nature may be more difficult to detect. FlexScan employs a flexible scan statistic using an adjacency matrix design.

Objective

To use the NYC DOHMH's ED syndromic surveillance data to evaluate FleXScan’s flexible scan statistic and compare it to results from the SaTScan circular scan. A second objective is to improve cluster detection in by improving geographic characteristics of the input files.

Syndromic Surveillance has been in use in New York City since 2001, with 2.5 million visits reported from 39 participating emergency departments, covering an estimated 75% of annual visits. As syndromic surveillance becomes increasingly spatial and tied to geography, the resulting spatial analysis is also evolving to provide new methodology and tools. In late 2004, the New York City Department of Health and Mental Hygiene (DOHMH) created the geographic information systems (GIS) Center of Excellence to identify ways in which GIS could enhance programs like syndromic surveillance. The DOHMH uses the SaTScan program for much of its spatial analysis (i.e. cluster analysis).

Objective

This paper describes a series of visualization enhancements and automation processes to efficiently depict syndromic surveillance data in GIS. Modelling the portrayal of events when merging existing syndromic surveillance with GIS can standardize and expedite results.

Arthropod-borne diseases such as malaria, dengue, Chagas disease, filariasis, leishmaniasis, and trypanosomiasis place tremendous public health burdens upon developing countries. The operational value of Decision Support Systems for management of these and other arthropod-borne diseases is enhanced by a Geographic Information System (GIS) spatial backbone allowing for visualization of spatiotemporal arthropod vector and disease patterns. However, resource-poor environments in desperate need of GIS-based solutions to more effectively manage arthropod-borne diseases can be faced with the reality that even the most basic GIS data are lacking and that investment in the infrastructure (high end computers, sophisticated GIS software, technical personnel) needed to develop such data is costprohibitive. This problem was addressed by use of Google Earth which freely provides access to both satellite imagery and mapping tools capable of generating polygons, lines and placemarks.

Objective

As part of a Dengue Decision Support System project funded by the Innovative Vector Control Consortium, we used satellite imagery and mapping tools freely available through Google Earth to: 1) generate data for basic city structure that could be imported into a GIS; and 2) serve as the spatial underpinning of a Decision Support System for arthropod-borne disease management.

The University of Washington has been working since 2000 with partners in Washington State to advance bioterrorism (BT) detection and preparedness. This project collects data on patients presenting with influenza-like illnesses and other potentially BT-related syndromes at emergency departments and primary care clinics (Kitsap, Clallam, and Jefferson counties) using a secure automated informatics approach. Local health jurisdiction epidemiologists use a web-based interface to view de-identified data and use a version of CDC’s EARS disease detection algorithms to watch for variances in patterns of diagnoses, volume, time and space as part of the public health real-time disease surveillance system. This processed hospital data is also made available back to the officials and administrators at the reporting hospital.

Objective

To understand GIS issues in a rural-tourban setting and demonstrate limitations of ZIPcode-only approaches compared to census tract and block approaches.

When a chemical or biological agent with public health implications is detected in the City of Houston, analysis of syndromic surveillance data is an important tool for investigating the authenticity of the alert, as well as providing information regarding the extent of contamination.

Syndromic surveillance data in Houston is currently provided by the Real-Time Outbreak Disease Surveillance, which collects and synthesizes real-time chief complaint data from 34 area hospitals, representing approximately 70% coverage of licensed ER beds in Harris County. Data collected for each complaint includes patient home and work zip codes, allowing for geographic analysis of the data in the case of a localized environmental contamination.

Historically, when alerted to a contaminant in the Houston area, the Houston Department of Health and Human Services (HDHHS) has analyzed health data for each zip code in the geographic area of interest separately, a time-intensive process.

Recognizing the need for a more accurate and timely response to an environmental alert, HDHHS proposes aggregating zip codes into zones, based on coverage of population and areas of high risk. These “Surveillance Zones” will be used to quickly reference syndromic data in the event of a chemical or biological event.

Objective

This paper discusses the development of zones within the City of Houston in order to more quickly and accurately reference surveillance data in the case of chemical or biological events.

Heat related illness is the number one cause of human death in relation to extreme weather events in the United States, resulting in an average of 400 deaths per year over the past few decades. It is also expected that both the duration and intensity of these events will increase. The temperature of the surface is measurable from a number of space borne satellites and can be derived using a number of available algorithms. This type of data can be compared to census collected variables to determine the number of persons at risk for heat related morbidity and mortality within urban environments.

Objective

This paper describes a method of determining areas at risk during extreme urban heat events using remote sensing technologies, geographical information systems and artificial neural networks.

Health care workers (HCWs) have an increased risk of exposure to infectious agents including (among others) tuberculosis, influenza, norovirus, and Clostridium difficile as a consequence of patient care1,2 Most occupational transmission is associated with violation of one or more basic principles of infection control: handwashing; vaccination of HCWs; and prompt isolation.3 OH surveillance is paramount in guiding efforts to improve worker safety and health and to monitor trends and progress over time.4 GIS can assist in supporting health situation analysis and surveillance for the prevention and control of health problems, for example: by creating temporal-spatial maps of outbreaks, public health workers can visualize the spread of cases as the outbreak progresses; spatial/database queries allow for selection of a specific location or condition to focus public health resources.

Objective

This paper describes a GIS tool which maps the floors and departments of a Southeastern Ontario tertiary care hospital for the purpose of monitoring respiratory and gastrointestinal (GI)-related Occupational Health (OH) visits among hospital employees.

Classical disease monitoring in local public health jurisdictions has been based on a list of “notifiable diseases”, more or less consistent from state-to-state.  While laboratories’ compliance with this requirement is, in general, excellent, clinician reporting is extremely poor [1].  In most circumstances, laboratory reporting is inherently delayed (perhaps by weeks), and most leaders in infectious disease and bioterrorism believe that recognition of abnormal spatiotemporal patterns within hours is essential [2].  Syndromic surveillance systems based on analysis of statistical aberrations in diagnosis code, chief complaint, or analysis of other data streams have been proposed and tested, but have largely failed to meet criteria of timeliness, sensitivity and specificity [3].  In addition, the vast majority of syndromic surveillance systems do not include veterinary surveillance, which may be important given that the vast majority of diseases of human public health importance are zoonotic in origin.  Thus, we have tested the hypothesis put forward by Henderson that “the astute clinician” can serve as the best early-warning indicator [4], with minimal demands on clinician time while simultaneously providing situational awareness to the broad community of health care providers and political decision makers who require such information.

Objective

It is widely agreed that "situational awareness" in disease surveillance is essential for intervening early in an infectious disease (or intoxination) outbreak. We report on 3.5 years of experience of a clinician-based system in a 25,000 square mile area of northwest Texas, a mixed urban, semi-rural and agricultural setting.

This paper explores some visualization methods for characterizing spatial signals detected by SaTScan and discusses how these maps might aid in deciding whether to investigate a signal, as well as the scope and focus of the investigation.