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Asthma

The burden of asthma is a major public health issue, and of a wider interest particularly to public health practitioners, health care providers and policy makers, as well as researchers. The literature on forecasting of adverse respiratory health events like asthma attacks is limited. It is an unclear field; and there is a need for more research on the forecasting of the demand for hospital respiratory services.

Objective

This paper describes a framework for creating a time series data set with daily asthma admissions, weather and air quality factors; and then generating suitable lags for predictive multivariate quantile regression models (QRMs). It also demonstrates the use of root mean square error (RMSE) and receiver operating characteristic (ROC) error measures in selecting suitable predictive models.

Referenced File
Submitted by uysz on

Southwest states are prone to wildfires, dust storms, and high winds especially during the monsoon season (June- September). Wildfire smoke is a complex mixture of carbon monoxide, carbon dioxide, water vapor, hydrocarbons, nitrogen, oxides, metals, and particulate matter (PM). Dust storms are made up of aerosols and dust particles varying in size; particles bigger than 10 µm are not breathable, but can damage external organs such as causing skin and eye irritations. Particles smaller than 10 µm are inhalable and often are trapped in the nose, mouth, and upper respiratory tracts, and can cause respiratory disorders such as asthma and pneumonia. Numerous studies have characterized the epidemiological and toxicological impact of exposure to PM in dust or smoke form on human health. All of these environmental conditions can have impacts on cardiovascular conditions such as hypertension and cause respiratory flare ups, especially asthma. Previous studies have shown a relationship between PM exposure and increases in respiratory-related hospital admissions. In an analysis of the health effects of a large wildfire in California in 2008, Reid, et. al, observed a linear increase in risk for asthma hospitalizations (RR=1.07, 95% CI= (1.05, 1.10) per 5 µg/m3 increase) and asthma emergency department visits (RR=1.06, 95% CI=(1.05, 1.07) per 5 µg/m3 increase) with increasing PM2.5 during wildfires. In a study specific to New Mexico, Resnick, et. al, found that smoke from the Wallow fire in Arizona in 2011 impacted the health of New Mexicans, observing increases in emergency department visits for asthma flare-ups in Santa Fe, Espanola, and Albuquerque residents. This current study will evaluate the effectiveness of outreach to asthmatic members during times of poor air quality; informing them of the air quality, instructing them to limit their outdoor activity, and to remind them to carry or access their inhalers or other medical necessities if/when needed.

Objective: To inform asthmatic, health plan patients of air quality conditions in their specific geographic location and to assess if the communication is successful in reducing the number of emergency department visits for asthmatic/respiratory flare ups.

Submitted by elamb on

The burden of asthma on the youngest children in Boston is largely characterized through hospitalizations and self-report surveys. Hospitalization rates are highest in Black and Hispanic populations under age five. A study of children living in Boston public housing showed significant risk factors, including obesity and pest infestation, with less than half of the study population being prescribed daily medication.

Information on asthma visits for children 5 years old or younger was requested by the Boston Public Health Commission Community Initiatives Bureau. The information is being used to establish a baseline for an integrated Healthy Homes Program that includes pest management and lead abatement. There is limited experience in using syndromic surveillance data for chronic disease program planning.

 

Objective

The objective of this study is to report on the use of syndromic surveillance data to describe seasonal patterns of asthma and health inequities among Boston residents, age five and under.

Referenced File
Submitted by hparton on

Tracking emergency department (ED) asthma visits is an important part of asthma surveillance, as ED visits can be preventable and may represent a failure of asthma control efforts. When using limited clinical ED datasets for secondary purposes such as public health surveillance, it is important to employ a standard approach to operationally defining ED visits attributable to asthma. The prevailing approach uses only the primary ICD-9-CM diagnosis assigned to the ED visit; however, doing so may underestimate the public health impact of asthma. We conducted this pilot study to determine the value of including ED visits with asthma-related diagnoses in secondary or tertiary positions. For example, for an ED visit with a primary diagnosis of upper respiratory infection and secondary diagnosis of asthma, it is possible that the infection triggered the asthma exacerbation and the visit could be attributed to both infection and asthma.

 

Objective

Determine operational definition of ED visits attributable to asthma for public health surveillance purposes.

Referenced File
Submitted by elamb on

Under a grant from the Centers for Disease Control and Prevention (CDC), the DC DOH established the Environmental Public Health Tracking Program (EPHTP) to monitor specific environmental and public health indicators and to investigate any potential links for the purpose of guiding policy development, resource allocation, and decision-making on disease prevention and treatment activities. This information improves understanding of the immediate and short-term effects of airborne pollutants on health care usage. In a collaborative project between JHU/APL and DC DOH, investigators explored and quantified correlations between ambient air quality measurements from five DC stations between October 2001 and March 2004 and DC hospital pediatric emergency department (ED) visits for asthma exacerbations. 

 

Objective

The study objective was to provide the CDC results from the EPHTP on quantifying the relationship between air quality and pediatric ED visits for asthma among DC residents over a 3 year period. This effort also explored novel uses of traditional data to understand background disease patterns so that unexpected fluctuations could be better detected in community disease trends and thereby identify early disease outbreaks.

Referenced File
Submitted by elamb on

In 2006, approximately 6.8 million children and 16.1 million adults were reported to have asthma in the US. The CDC BioSense System currently receives data from >540 hospital emergency departments (EDs; 522 send patient chief complaints and 182 send physician diagnoses), and captures about 11% of all U.S. ED visits.

 

OBJECTIVE

To describe the potential utility of BioSense data for surveillance of asthma.

Referenced File
Submitted by elamb on

Over the last decade, the application of syndromic surveillance systems has expanded beyond early event detection to include longterm disease trend monitoring. However, statistical methods employed for analyzing syndromic data tend to focus on early event detection. Generalized linear mixed models (GLMMs) may be a useful statistical framework for examining long-term disease trends because, unlike other models, GLMMs account for clustering common in syndromic data, and GLMMs can assess disease rates at multiple spatial and temporal levels (1). We show the benefits of the GLMM by using a GLMM to estimate asthma syndrome rates in New York City from 2007 to 2012, and to compare high and low asthma rates in Harlem and the Upper East Side (UES) of Manhattan.

Objective:

Show the benefits of using a generalized linear mixed model (GLMM) to examine long-term trends in asthma syndrome data.

 

Submitted by Magou on

A socio-marker is a measurable indicator of social conditions where a patient is embedded in and exposed to, being analogous with a biomarker indicating the severity or presence of some disease state. Social factors are one of the most clinical health determinants, which play a critical role in explaining health outcomes. Socio-markers can help medical practitioners and researchers to reliably identify high-risk individuals in a timely manner.

Objective:

Asthma is one of the most common chronic childhood diseases in the United States. In addition to its pervasiveness, pediatric asthma shows high sensitivity to the environment. Combining medical-social dataset with machine learning methods we demonstrate how socio-markers play an important role in identifying patients at risk of hospital revisits due to pediatric asthma within a year.

Submitted by elamb on

The negative effect of air pollution on human health is well documented illustrating increased risk of respiratory, cardiac and other health conditions. Currently, during air pollution episodes Public Health England (PHE) syndromic surveillance systems provide a near real-time analysis of the health impact of poor air quality. In England, syndromic surveillance has previously been used on an ad hoc basis to monitor health impact; this has usually happened during widespread national air pollution episodes where the air pollution index has reached "High"™ or "Very High"™ levels on the UK Daily Air Quality Index (DAQI). We now aim to undertake a more systematic approach to understanding the utility of syndromic surveillance for monitoring the health impact of air pollution. This would improve our understanding of the sensitivity and specificity of syndromic surveillance systems for contributing to the public health response to acute air pollution incidents; form a baseline for future interventions; assess whether syndromic surveillance systems provide a useful tool for public health alerting; enable us to explore which pollutants drive changes in health-care seeking behaviour; and add to the knowledge base.

Objective:

To explore the utility of syndromic surveillance systems for detecting and monitoring the impact of air pollution incidents on health-care seeking behaviour in England between 2012 and 2017.

Submitted by elamb on

Recently, a growing number of studies have made use of Twitter to track the spread of infectious disease. These investigations show that there are reliable spikes in traffic related to keywords associated with the spread of infectious diseases like Influenza [1], as well as other Syndromes [2]. However, little research has been done using Social Media to monitor chronic conditions like Asthma, which do not spread from sufferer to sufferer. We therefore test the feasibility of using Twitter for Asthma surveillance, using techniques from NLP and machine learning to achieve a deeper understanding of what users Tweet about Asthma, rather than relying only on keyword search.

Objective

We present a Content Analysis project using Natural Language Processing to aid in Twitter-based syndromic surveillance of Asthma

Submitted by rmathes on
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