Data Analytics

Viral hepatitis is a global public health problem affecting millions of people every year, causing disability and death [1].The hepatitis B virus (HBV) is transmitted through the contact with the blood or other body fluids of an infected person. For formulating evidence-based policy of Public Health and data for action we should know about main ways of transmission HBV and population group with high risk of infection.

Objective

To develop model to study risk factors for hepatitis B (HB) and to identify the main causes affecting the incidence of HB.

Syndromic surveillance involves the analysis of time series of health indicators to identify changes in disease patterns. To this end, statistical modeling is used to reduce systematic data variation. Still, there is variation that cannot be accounted for in this approach, e.g. mass gatherings, extreme weather and other high-profile events. To filter sporadic events, data transformation can be applied, e.g. proportion data from correlated data streams (Peter, Najmi and Burkom, 2011; Reis, Kohane and Mandl, 2007). However, we lack systematic criteria for applying data transformations, e.g. ratios versus geometric means. To develop guidelines, we conducted a power analysis and compared the results with empirical findings (Andersson et al, 2013).

Objective

For the purpose of optimizing baselines for point-source outbreak detection, we carried out a power analysis of the effects of data transformations. More specifically, the aim was to develop statistical criteria for using composite baselines, i.e. ratios and geometric means of data streams. The results were validated by outbreak data on acute gastroenteritis (The Swedish National Telephone Health Service 1177).

Newborns who fail initial hearing screening are encouraged to follow up to get further tests to confirm diagnosis of hearing loss. Loss to follow-up of hearing screening is defined as a newborn who fails the initial hearing test before hospital discharge but does not follow up with further hearing tests to confirm diagnosis of hearing loss. Although rates of loss to follow-up are considered high in Louisiana, with great efforts by Louisiana Early Hearing Detection and Intervention Program (EHDI), loss to follow-up has significantly improved in the last several years. A trend analysis of loss to follow-up was helpful in evaluating accomplishments to reduce loss to follow-up and refining plans and strategies to enhance the quality of newborn hearing detection and intervention in the state.

Objective

The study aimed to analyze the trend in loss to follow-up of hearing screening among newborns who failed initial hearing screening tests prior to hospital discharge, and evaluate accomplishments in improving loss to follow-up from 2004 to 2011 in Louisiana.

A goal of biosurveillance is to identify incidents that require a public health response. The challenge is creating specific definitions of such incidents so they can be detected. In syndromic surveillance, this is accomplished by classifying emergency department chief complaints, nurse triage calls, and other prediagnostic data into categories, and then looking for increases in visits related to those categories. This approach can only find incidents that match those predefined categories. It is well-suited to handle common diseases; data from prior years provides information not only on which symptoms correlate with the disease, but also on how patients report them and how they appear in prediagnostic data streams. For unique or rare events, it is hard to know in advance how they will be described or recorded. Another approach is to look for similarities in the time of the healthcare encounters alone. This method can detect events which are missed by syndrome-oriented surveillance, but healthcare encounters that only have time of occurrence aren't necessarily related. To address this limitation, we propose a set of similarity criteria which incorporates both timing and reason.

Objective

Develop a method for detecting groups of related healthcare encounters without having to specify details of the reasons for those encounters in advance.

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.

Infectious disease surveillance is a process, the product of which reflects both real illness and public awareness of the disease (Figure 1). According to our previous research studies [1,2], decisions made by patients, healthcare providers, and public health professionals about seeking and providing healthcare and about reporting cases to health authorities are all influenced by the information environment, which changes constantly. Biases are therefore imbedded in each surveillance systems, and need to be assessed to provide better situational awareness for decision-making.

Objective

Our goal is to develop a statistical framework to characterize influenza surveillance systems and their sensitivity to information environment.

The NYC syndromic surveillance system has been monitoring syndromes from NYC emergency department (ED) visits for over a decade. We applied several aberration detection methodologies to a time series of ED visits in NYC spiked with synthetic outbreaks. This effort is part of a larger evaluation of the NYC syndromic system, funded by a grant from the Alfred P. Sloan Foundation.

Objective

To critically evaluate temporal aberration detection methodologies using New York City (NYC) syndromic surveillance data.

To explore the practicality of using a spatio-temporal scan statistic within a health board, rather than academic or national setting.

As part of the epidemiological investigation of an outbreak of Q fever in a factory in Scotland, we aimed to utilise a spatial scan statistic to aid in identification of areas associated with increased relative risk of infection.

This paper describes several interactive data exploration tools for examining surveillance data.