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Public Health Surveillance

Completeness of public health information is essential for the accurate assessment of community health progress and disease surveillance. Yet challenges persist with respect to the level of completeness that public health agencies receive in reports submitted by health care providers. Missing and incomplete data can jeopardize information reliability and quality resulting in inaccurate disease evaluation and management (1). Additionally, incomplete data can prolong the time required for disease investigators to complete their work on a reported case. Thus, it is important to determine where the scarcity of information is coming from to recognize the characteristics of provider reporting.

Objective

To examine the completeness of data elements required for notifiable disease surveillance from official, provider-based reports submitted to a local health department.

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Presented December 13, 2018.

For public health surveillance, is machine learning worth the effort? What methods are relevant? Do you need special hardware? This talk was motivated by these and other questions asked by ISDS members. It will focus on providing practical—and slightly opinionated—advice about how to determine whether machine learning could be a useful tool for your problem.

Presenter

The NNDSS is the public health surveillance system that enables all levels of public health (local, state, territorial, and federal) to monitor the occurrence and spread of the diseases and conditions that the Council of State and Territorial Epidemiologists (CSTE) has officially designated as being "nationally notifiable". The NNDSS data are a critical source of data for monitoring disease trends, effectiveness of prevention and control programs, and policy development. To provide timely NNDSS data, state and territorial health departments voluntarily report notifiable disease incidence data to CDC when they become aware of these cases and as per recommended national notification timeframes. These provisional data are published each week in Morbidity and Mortality Weekly Report (MMWR). Great strides have been made exploring and exploiting new and different sources of disease surveillance data and developing robust statistical methods for analyzing the collected data (1). However, there have been fewer efforts in the area of on-line dissemination of surveillance data, which is so important in maximizing the utility of collected data.

Objective

The purpose of this project was to identify ideas and potential options for an enhanced dissemination of provisional data for the US National Notifiable Diseases Surveillance System (NNDSS).

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The importance transmitting clinical information to public health for disease surveillance is well-documented. Conventional reporting processes require health care providers to complete paper-based notifiable condition reports which are transmitted by fax and mail to public health agencies. These processes result in incomplete reports, inconsistencies in reporting frequencies among different diseases and reporting delays as well as time-consuming follow-up by public health to get needed information. One strategy to address these issues is to electronically pre-populate report forms with available clinical, lab and patient data to streamline reporting workflows, increase data completeness and, ultimately, provide access to more timely and accurate surveillance data for public health organizations. Prior to implementing an intervention that includes using pre-populated forms, we conducted interviews in clinical and public health settings to identify the barriers and facilitators to adopting and utilizing the forms and their potential impact on workflow and perceived burden. These interviews are a component of a larger mixed methods evaluation that will triangulate pre- and post-intervention quantitative data quality measures with qualitative results.

Objective

Introduction of new health information technologies can produce unanticipated consequences on existing user behaviors, workflow, etc. Prior to implementing a public health reporting intervention, we conducted a series of interviews regarding workflow and perceptions of task burden with respect to notifiable condition reporting.

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Meaningful Use has increased interest in submission of ELR to public health agencies, prompting these agencies to analyze their reporting process. Tennessee’s reporting regulations require anyone with knowledge of or suspecting a reportable disease or event report to the local health department. Although it is understood that laboratories are more diligent and routine reporters, focus in listing of these events is from the healthcare provider perspective. Public health agencies must acknowledge the differences in provider case reporting and laboratory result reporting. Despite Tennessee Department of Health's (TDH) required use of standardized vocabulary for ELR such as Logical Observation Identifiers Names and Codes (LOINC) to identify the test performed and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) to identify organism names, ordinal results, and specimen type, internally inconsistent information in messages has been identified. For example, a performed test with LOINC value 13950-1 encodes for a hepatitis A virus IgM antibody test from serum or plasma using an enzyme immunoassay (EIA) and calls for an ordinal result. However the sender describes a Hepatitis C Antibody (Anti HCV) test and provides a numeric result. In order to achieve semantic understanding of the actionable content of ELR messages, a systematic means to document and validate vocabulary is needed.

Objective

To develop a means for validating standardized vocabulary used to report laboratory events via prescribed electronic laboratory reporting (ELR) standards and implementation guides in order to limit internally inconsistent information within ELR messages intended for public health action.

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National telephone health advice service data have been investigated as a source for syndromic surveillance of influenza-like illness and gastroenteritis . Providing a high level of coverage, the system might serve as an early outbreak detection tool. We have previously found that telephone triage service data of acute gastroenteritis was superior to web queries as well as over-the-counter pharmacy sales of anti-diarrhea medication to detect large water- and foodborne outbreaks of gastrointestinal illness in Sweden during the years 2007–2011 (4). However, information is limited regarding the usefulness, characteristics, and signal properties of local telephone triage data for monitoring and identifying outbreaks at the community level.

Objective

Our aim was to use telephone triage data to develop a model for community-level syndromic surveillance that can detect local outbreaks of acute gastroenteritis (AGE) and influenza-like illness (ILI) and allow targeted local disease control information.

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The increasing use of the Internet to arrange sexual encounters presents challenges to public health agencies formulating STD interventions, particularly in the context of anonymous encounters. These encounters complicate or break traditional interventions. In previous work [1], we examined a corpus of anonymous personal ads seeking sexual encounters from the classifieds website Craigslist and presented a way of linking multiple ads posted across time to a single author. The key observation of our approach is that some ads are simply reposts of older ads, often updated with only minor textual changes. Under the presumption that these ads, when not spam, originate from the same author, we can use efficient near-duplicate detection techniques to cluster ads within some threshold similarity. Linking ads in this way allows us to preserve the anonymity of authors while still extracting useful information on the frequency with which authors post ads, as well as the geographic regions in which they seek encounters. While this process detects many clusters, the lack of a true corpus of authorship-linked ads makes it difficult to validate and tune the parameters of our system. Fortunately, many ad authors provide an obfuscated telephone number in ad text (e.g., 867-5309 becomes 8sixseven5three oh nine) to bypass Craigslist filters, which prohibit including phone numbers in personal ads. By matching phone numbers of this type across all ads, we can create a corpus of ad clusters known to be written by a single author. This authorship corpus can then be used to evaluate and tune our existing near-duplicate detection system, and in the future identify features for more robust authorship attribution techniques.

Objective:

This paper constructs an authorship-linked collection or corpus of anonymous, sex-seeking ads found on the classifieds website Craigslist. This corpus is then used to validate an authorship attribution approach based on identifying near duplicate text in ad clusters, providing insight into how often anonymous individuals post sexseeking ads and where they meet for encounters.

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The use of health information systems to electronically deliver clinical data necessary for notifiable disease surveillance is growing. For health information systems to be effective at improving population surveillance functions, semantic interoperability is necessary. Semantic interoperability is “the ability to import utterances from another computer without prior negotiation” (1). Semantic interoperability is achieved through the use of standardized vocabularies which define orthogonal concepts to represent the utterances emitted by information systems. There are standard, mature, and internationally recognized vocabularies for describing tests and results for notifiable disease reporting through ELR (2). Logical Observation Identifiers Names and Codes (LOINC) identify the specific lab test performed. Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) identify the diseases and organisms tested for in a lab test. Many commercial laboratory and hospital information systems claim to support LOINC and SNOMED CT on their company websites and in marketing materials, and systems certified for Meaningful Use are required to support LOINC and SNOMED CT. There is little empirical evidence on the use of semantic interoperability standards in practice.

Objective:

To characterize the use of standardized vocabularies in real-world electronic laboratory reporting (ELR) messages sent to public health agencies for surveillance.

 

Submitted by Magou on

The American Recovery and Reinvestment Act of 2009 authorized the Centers for Medicare and Medicaid Services (CMS) to incentivize hospitals and physicians to become meaningful users of electronic health record (EHR) systems. In a final rule issued August 2012, CMS outlined the requirements for Stage 2 meaningful use to be effective in 2014 (1). The Stage 2 criteria require eligible hospitals to submit electronic laboratory reports to health departments. While many state health departments receive some portion of notifiable disease reports electronically, the final Stage 2 rule is likely to increase the volume of incoming electronic reports. The Centers for Disease Control and Prevention are urging health departments to prepare for the sharp increase in electronic laboratory reporting (ELR). Crucial to preparedness is estimation of how many ELR reports can be expected. However, few health departments have experience with high volume ELR, making estimation difficult. The Indiana Network for Patient Care (INPC), a regional health information exchange, has been processing high volumes of ELR for over a decade (2). To support health departments estimate potential ELR increases, the INPC examined its current volumes from hospitals with advanced EHR capabilities.

Objective:

To support health department estimation of future electronic laboratory report volumes from hospitals that achieve Stage 2 meaningful use.

 

Submitted by Magou on

Regional poison control centers (RPC) receive calls about a variety of poisoning exposures. Callers’ symptoms may not otherwise enter traditional public health (PH) surveillance systems. I report a 16-week pilot study of a new tool to enable the RPC to analyze and integrate call data with the PH, to augment ongoing disease surveillance efforts.

Objective

A new tool allowing analysis of poison control center data and integration of that data into public health surveillance efforts is described.

Referenced File
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