Tuberculosis

Mongolia is one of countries in the WHO Western Pacific Region with a high TB burden. The National Stop TB Strategy 2010-2015 implemented and developed strong surveillance and response system in the country. However, new TB incidence and deaths have not decreased significantly.Political commitment is critical for effective TB surveillance and that commitment can be demonstrated by a country's legal framework, which governs the practice of prevention and control. Therefore, this study is aimed at investigating the legal underpinnings for disease surveillance to help identify what policy changes have occurred in Tuberculosis surveillance.

Objective: In order to track progress towards TB goals, weinvestigated the legal framework for disease surveillance, specifically policy changes for TB surveillance in Mongolia during the MDGs and the SDGs era.

A new TB case can be classified as: 1) a source case for transmission leading to other, secondary active TB cases; 2) a secondary case, resulting from recent transmission; or 3) an isolated case, uninvolved in recent transmission (i.e. neither source nor recipient). Source and secondary cases require more intense intervention due to their involvement in a chain of transmission; thus, accurate and rapid classification of new patients should help public health personnel to effectively prioritize control activities. However, currently accepted method for the classification, DNA fingerprint analysis, takes many weeks to produce the results; therefore, public health personnel often solely rely on their intuition to identify the case who is most likely to be involved in transmission. Various clinical and socio-demographic features are known to be associated with TB transmission. By using these readily available data at the time of diagnosis, it is possible to rapidly estimate the probabilities of the case being source, secondary, and isolated.

Objective

To develop and validate a prediction model which estimates the probability of a newly diagnosed tuberculosis (TB) case being involved in ongoing chain of transmission, based on the case's clinical and socio-demographic attributes available at the time of diagnosis.

Immigrants and refugees are required to undergo tuberculosis (TB) screening before entering the United States (US)(1). Nearly 700 overseas panel physicians designated by the US Department of State, perform these screenings (2). Over 450,000 new immigrants and nearly 80,000 refugees arrive in the US each year with their medical documentation (3). Those with culture positive or smear positive TB are treated prior to arrival in the US; those with radiographic or symptomatic TB but negative smears and culture may enter the US with Class B TB(4). After arrival, CDC's Electronic Disease Notification system (EDN) notifies the health departments of the results of overseas examinations.

Objective

To conduct preliminary assessment of the US destination locations among newly arriving immigrants and refugees with tuberculosis classifications

The Electronic Surveillance System for the Early Notification of Community-based Epidemics in Florida (ESSENCE-FL) is a web-based application for use by public health professionals within the Florida Department of Health (FDOH). The main source of data for ESSENCE-FL is emergency department (ED) data. Ten hospitals in Hillsborough County, Florida send their data to the ESSENCE-FL server. ESSENCE-FL requires only a limited data set to be sent by the hospital which includes patient chief complaint (CC) and discharge diagnosis (DD). These fields can be searched individually, in separate queries, to identify possible records of interest. These two fields have been concatenated to create the single chief complaint and discharge diagnosis (CCDD) field, allowing both fields to be searched with a single query.

Objective

While syndromic surveillance systems were originally designed for the detection of outbreaks and clusters of illness, they have been found to be useful at identifying unreported conditions of public health importance. Within the Florida Department of Health in Hillsborough County (FDOH-Hillsborough), these conditions of public health importance have primarily focused on the reportable diseases and conditions that fall under the responsibility of the Epidemiology Program and have not included tuberculosis. A specific query has been developed to search for and identify possible tuberculosis patients and exposed contacts. This study is designed to determine the usefulness of specific-term chief complaint and discharge diagnosis (CCDD) queries in identifying tuberculosis patients and exposed contacts.

The former Soviet Union (FSU)—through the Sanitary-Epidemiologic Service (SES)—developed an extensive system of disease surveillance that was effective, yet centrally planned in Moscow. Even after the fall of the FSU in 1991, most newly independent states maintained all or parts of the SES structure. However, even 15 years later, the loss of economic and technical assistance from Moscow has negatively impacted the effectiveness and efficiency of disease surveillance in these republics, including Armenia and Georgia. In 2005, Armenia and Georgia reported tuberculosis (TB) incidences of 71 and 83, respectively, per 100,000.

Objective

To enhance its effectiveness and efficiency, we evaluated TB surveillance in the FSU Republics of Armenia and Georgia.

Tuberculosis (TB) has reemerged as a global health epidemic in recent years. Although several researchers have examined the use of space-time surveillance to detect TB clusters, they have not used genetic information to verify that detected clusters are due to person-to-person transmission. Using genetic fingerprinting data for TB cases, we sought to determine whether detected clusters were due to recent transmission.

Objective

This paper describes the utility of prospective spacetime surveillance to detect genetic clusters of TB due to person-to-person spread.

Tuberculosis (TB) has reemerged as a global public health epidemic in recent years. TB remains a serious public health problem among certain patient populations, and is prevalent in many urban areas. The World Health Organization estimates that approximately nine million individuals will develop active TB disease and more than two million will die from TB. The global burden of TB remains enormous, and will likely rank high among public health problems in the coming decades. Although evaluating local disease clusters leads to effective prevention and control of TB, there are few, if any, spatiotemporal comparisons for epidemic diseases. In this study, we used the space-time scan statistic to identify where and when the prevalence of TB is high in Fukuoka Prefecture. The ability to detect disease outbreaks is important for local and national health departments to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Because the statistic meets these needs completely, results that are effective and practical for public health officials are expected from this study.

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.

Approximately one quarter of people treated for tuberculosis (TB) have no supporting microbiology, and thus are not detectable through laboratory reporting systems. Health departments depend upon clinicians to report these cases, but there is important underreporting. We previously described the performance of several algorithms for TB detection using electronic medical record (EMR) and claims data, and noted good sensitivity when screening for >2 anti-TB drugs; however, the positive predictive value was only 30%. We re-evaluated this and other algorithms in light of evolving TB treatment practices and enhanced ability to apply complex decision rules to EMR data in real time.

Objective

To develop algorithms for case detection of TB using EMR data to improve notifiable disease reporting.

Learn about a highly infectious resistant tuberculosis outbreak among recent immigrants & the multijurisdictional public health response. Recognize basics about tuberculosis & anticipate difficulties with immigrants and resistant strains. Enhance epidemiologic response and treatment of tuberculosis that emerged across borders requiring coordinated response from employers, government, and individuals.