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Polgreen Linnea

Description

Disease screening facilitates the reduction of disease prevalence in two ways: (1) by preventing transmission and (2) allowing for treatment of infected individuals. Hospitals choosing an optimal screening level must weigh the benefits of decreased prevalence against the costs of screening and subsequent treatment. If screening decisions are made by multiple decision units (DU, e.g., hospital wards), they must consider the disease prevalence among admissions to their unit. Thus, the screening decisions made by one DU directly affect the disease prevalence of the other units when patients are shared. Because of this interdependent relationship, one DU may have an incentive to "free-ride" off the screening decisions of others as the disease prevalence declines. On the other hand, DUs may find it futile to invest in screening if they admit a large number of infected patients from neighbors who fail to screen properly. This problem is important in determining the optimal level of unit autonomy, since increasing a unit's level of autonomy in screening effectively increases the total number of DUs.

 

Objective

To analyze optimal disease screening in strategic multi-unit settings, and determine how the level of unit autonomy may effect screening decisions.

Submitted by elamb on
Description

Adverse drug events (ADEs) are a major cause of morbidity and mortality. However, post-marketing surveillance systems are passive and reporting is generally not mandated. Thus, many ADEs go unreported, and it is difficult to estimate and/or anticipate side effects that are unknown at the time of approval. ADEs that are reported to the FDA tend to be severe, and potentially common, but less serious side effects are more difficult to characterize and document. Drugs with a high risk of harm outweighing the therapeutic value have recently been subjected to a greater level of interest with the Food and Drug Administration's Risk Evaluation and Mitigation Strategies (REMS). However, no rapid method to detect if the REMS produce the desired effect and assessment of the impact is conducted by the drug manufacturer. Increasingly, Americans have been turning to the internet for health related information, largely by the use of search engines such as Google. The volume of searches for drugs and ADEs provides a unique insight about the interest in various medications and side effects as well as longitudinal changes.

 

Objective

To investigate the use of search volume data from Google Insight for the detection and characterization of adverse drug events.

Submitted by elamb on
Description

Adverse drug events (ADEs) are a significant source of morbidity and mortality. The majority of post-marketing surveillance for ADEs is passive. Information regarding ADEs is reported to the medical community in peer-reviewed journals. However, in most cases there is significant lag in the publication of peer-reviewed articles concerning ADEs. Within medical journals, our intuition is that letters to the editor may provide the earliest reports of ADEs. They often report single case reports or a collection of cases and usually precede more formal investigations and reports. Although these letters may contain useful and timely information, the challenge is that letters to the editor may be "buried" inside print journals. Furthermore, they may be more difficult to find and access even when using electronic searches because unlike other published reports, there is no corresponding abstract to view. Due to the lack of an abstract, detection depends almost exclusively upon words in a title, or manually applied Medical Subject Headings (MeSH). We propose that searching the full text of letters to the editor can provide a faster and perhaps more complete detection of ADEs compared to searches based on MeSH terms or titles alone.

Objective

Our objective was to explore the intuition that letters to the editor in leading medical journals contain early signals about adverse drug events. We explored this with letters in two leading journals.

Submitted by elamb on
Description

Investigators have used the volume of internet search queries to model disease incidence, especially influenza and general consumer behavior [1]. Our group has used search volume to model interest in FDA safety alerts and adverse drug event incidence. We found evi- dence of changes in search behavior following warnings and the ex- pected relationship between search volume and adverse drug event incidence. Thus, search volume may help provide near real time sur- veillance of drug use patterns to help monitor and mitigate risk to the population from adverse drug events. However, the use of search query volume as a proxy for drug use has yet to be validated.

We attempt to validate search volume estimation of drug utilization in three ways: 1) explore seasonal variations in search volume and outpatient utilization, 2) monitor change between substitute drugs fol- lowing patent expirations and 3) use search volume estimation meth- ods to estimate TB incidence.

Objective

To validate search volume estimation for outpatient medication prescribing.

Submitted by dbedford on
Description

Historically, patients with TB have often been diagnosed late or after death. This delay in diagnosis often occurs because TB is misdiagnosed as an alternative respiratory illness (RI), such as pneumonia . TB infected patients that are not correctly diagnosed when initial symptoms occur may spread infection to others in both healthcare settings and the community.

Objective

To estimate the potential number of Tuberculosis (TB) cases that occur in inpatient and emergency department settings that are missed, diagnosed as something else, go untreated and return to the community, prior to receiving a correct diagnosis of TB. We analyze inpatient and emergency department records from the state of California from 2005-2011.

Submitted by teresa.hamby@d… on