Search

Climate change is already impacting the North American Great Lakes ecosystem and understanding the relationship between climate events and public health, such as waterborne acute gastrointestinal illnesses (AGIs), can help inform needed adaptive capacity for drinking water systems (DWSs). In this study, we assessed a harmonized binational dataset for the effects of extreme precipitation events (≥90th percentile) and preceding dry periods, source water turbidity, total coliforms, and protozoan AGIs – cryptosporidiosis and giardiasis – in the populations served by four DWSs that source surface water from Lake Ontario (Hamilton and Toronto, Ontario, Canada) and Lake Michigan (Green Bay and Milwaukee, Wisconsin, USA) from January 2009 through August 2014. We used distributed lag non-linear Poisson regression models adjusted for seasonality and found extreme precipitation weeks preceded by dry periods increased the relative risk of protozoan AGI after 1 and 3–5 weeks in three of the four cities, although only statistically significant in two. Our results suggest that the risk of protozoan AGI increases with extreme precipitation preceded by a dry period. As extreme precipitation patterns become more frequent with climate change, the ability to detect changes in water quality and effectively treat source water of varying quality is increasingly important for adaptive capacity and protection of public health.

Measuring clinically relevant opioid-related problems in health care systems is challenging due to the lack of standard definitions and coding practices. Well-defined, opioid-related health problems (ORHPs) would improve prevalence estimates and evaluation of clinical interventions, crisis response, and prevention activities. We sought to estimate prevalence of opioid use disorder (OUD), opioid misuse, and opioid poisoning among inpatients at a large, safety net, health care institution.Our study included events documented in the electronic health records (EHRs) among hospitalized patients at Denver Health Medical Center during January 1, 2017 to December 31, 2018. Multiple EHR markers (ie, opioid-related diagnostic codes, clinical assessment, laboratory results, and free-text documentation) were used to develop diagnosis-based and extended definitions for OUD, opioid misuse, and opioid poisoning. We used these definitions to estimate number of hospitalized patients with these conditions.During a 2-year study period, 715 unique patients were identified solely using opioid-related diagnostic codes; OUD codes accounted for the largest proportion (499/715, 69.8%). Extended definitions identified an additional 973 unique patients (~136% increase), which includes 155/973 (15.9%) who were identified by a clinical assessment marker, 1/973 (0.1%) by a laboratory test marker, and 817/973 (84.0%) by a clinical documentation marker.Solely using diagnostic codes to estimate prevalence of clinically relevant ORHPs missed most patients with ORHPs. More inclusive estimates were generated using additional EHR markers. Improved methods to estimate ORHPs among a health care system's patients would more fully estimate organizational and economic burden to more efficiently allocate resources and ensure capacity to provide clinical services.

Learning health systems can help estimate chronic disease prevalence through distributed data networks (DDNs). Concerns remain about bias introduced to DDN prevalence estimates when individuals seeking care across systems are counted multiple times. This paper describes a process to deduplicate individuals for DDN prevalence estimates.We operationalized a two-step deduplication process, leveraging health information exchange (HIE)-assigned network identifiers, within the Colorado Health Observation Regional Data Service (CHORDS) DDN. We generated prevalence estimates for type 1 and type 2 diabetes among pediatric patients (0-17 years) with at least one 2017 encounter in one of two geographically-proximate DDN partners. We assessed the extent of cross-system duplication and its effect on prevalence estimates.We identified 218 437 unique pediatric patients seen across systems during 2017, including 7628 (3.5%) seen in both. We found no measurable difference in prevalence after deduplication. The number of cases we identified differed slightly by data reconciliation strategy. Concordance of linked patients' demographic attributes varied by attribute.We implemented an HIE-dependent, extensible process that deduplicates individuals for less biased prevalence estimates in a DDN. Our null pilot findings have limited generalizability. Overlap was small and likely insufficient to influence prevalence estimates. Other factors, including the number and size of partners, the matching algorithm, and the electronic phenotype may influence the degree of deduplication bias. Additional use cases may help improve understanding of duplication bias and reveal other principles and insights. This study informed how DDNs could support learning health systems' response to public health challenges and improve regional health.

BACKGROUND Timely, precise, and localized surveillance of nonfatal events is needed to improve response and prevention of opioid-related problems in an evolving opioid crisis in the United States. Records of naloxone administration found in prehospital emergency medical services (EMS) data have helped estimate opioid overdose incidence, including nonhospital, field-treated cases. However, as naloxone is often used by EMS personnel in unconsciousness of unknown cause, attributing naloxone administration to opioid misuse and heroin use (OM) may misclassify events. Better methods are needed to identify OM. OBJECTIVE This study aimed to develop and test a natural language processing method that would improve identification of potential OM from paramedic documentation. METHODS First, we searched Denver Health paramedic trip reports from August 2017 to April 2018 for keywords naloxone, heroin, and both combined, and we reviewed narratives of identified reports to determine whether they constituted true cases of OM. Then, we used this human classification as reference standard and trained 4 machine learning models (random forest, k-nearest neighbors, support vector machines, and L1-regularized logistic regression). We selected the algorithm that produced the highest area under the receiver operating curve (AUC) for model assessment. Finally, we compared positive predictive value (PPV) of the highest performing machine learning algorithm with PPV of searches of keywords naloxone, heroin, and combination of both in the binary classification of OM in unseen September 2018 data. RESULTS In total, 54,359 trip reports were filed from August 2017 to April 2018. Approximately 1.09% (594/54,359) indicated naloxone administration. Among trip reports with reviewer agreement regarding OM in the narrative, 57.6% (292/516) were considered to include information revealing OM. Approximately 1.63% (884/54,359) of all trip reports mentioned heroin in the narrative. Among trip reports with reviewer agreement, 95.5% (784/821) were considered to include information revealing OM. Combined results accounted for 2.39% (1298/54,359) of trip reports. Among trip reports with reviewer agreement, 77.79% (907/1166) were considered to include information consistent with OM. The reference standard used to train and test machine learning models included details of 1166 trip reports. L1-regularized logistic regression was the highest performing algorithm (AUC=0.94; 95% CI 0.91-0.97) in identifying OM. Tested on 5983 unseen reports from September 2018, the keyword naloxone inaccurately identified and underestimated probable OM trip report cases (63 cases; PPV=0.68). The keyword heroin yielded more cases with improved performance (129 cases; PPV=0.99). Combined keyword and L1-regularized logistic regression classifier further improved performance (146 cases; PPV=0.99). CONCLUSIONS A machine learning application enhanced the effectiveness of finding OM among documented paramedic field responses. This approach to refining OM surveillance may lead to improved first-responder and public health responses toward prevention of overdoses and other opioid-related problems in US communities.

Background Assessment of people affected by opioid-related problems and those receiving care is challenging due to lack of common definitions and scattered information. We sought to fill this gap by demonstrating a method to describe a continuum of opioid addiction care in a large, public safety-net institution. Methods Using 2017 clinical and administrative data from Denver Health (DH), we created operational definitions for opioid use disorder (OUD), opioid misuse (OM), and opioid poisoning (OP). Six stages along a continuum of patient engagement in opioid addiction care were developed, and operational definitions assigned patients to stages for a specific time point of analysis. National data was used to estimate the Denver population affected by OUD, OM and OP. Results In 2017, an estimated 6688 people aged ≥12 years were affected by OUD, OM, or OP in Denver; 48.4% (3238/6688) were medically diagnosed in DH. Of those, 32.5% (1051/3238) were in the medication assisted treatment stage, and, of those, 59.8% (629/1051) in the adhered to treatment stage. Among that latter group, 78.4% (493/629) adhered at least 90 days and 52.3% (329/629) for more than one year. Among patients who received medication assisted treatment, less than one third (31.3%, 329/1051) were adherent for more than one year. Conclusions A health-system level view of the continuum of opioid addiction care identified improvement opportunities to better monitor accuracy of diagnosis, treatment capacity, and effectiveness of patient engagement. Applied longitudinally at local, state and national levels, the model could better synergize responses to the opioid crisis.

Drawing on the experiences of hundreds of public health and primary care clinicians from across the United States, this book explains why population health is receiving so much attention from policy makers in states and federal agencies, the practical steps that clinicians and public health professionals can take to work together to meet the needs of their community, signs that you are on the right track (or not) and how to sustain successes to the benefit of patients, community members, and the health care and public health teams that care for them.

Study objective We demonstrate the feasibility and utility of emergency department (ED) syndromic surveillance using a regional emergency medicine Internet application to minimize impact on ED and public health staffing. Methods Regional (multi-ED) surveillance was established for 2 periods, one characterized by a high-profile national sports event and the other during an international disease outbreak. Counts of patient visits meeting syndrome criteria and total patient visits were reported daily on the secure regional emergency medicine Internet site and downloaded by public health staff. Trends were analyzed and displayed on the secure Web site. ED participants were surveyed about the acceptability and time cost of the project. Results In the first ("All Star Game") project, 8 departments reported daily counts for 4 weeks, covering more than 26,000 patient visits. In the second ("severe acute respiratory syndrome" [SARS]) project, an average of 11 departments in the same region reported daily data on febrile respiratory illnesses, travel, and contacts for 10 weeks. Experience with the first project allowed for rapid implementation of the second project during a 3-day period. In both instances, the surveillance efforts were undertaken without the need for extraordinary ED or public health staffing requirements. Conclusion A regional emergency medicine Internet approach permitted rapid implementation of multisite syndromic surveillance without additional staff. Some problems were identified with the first project, related to clinician checklist completion and manual data tabulation and entry. The SARS project addressed these by simplifying data collection and restricting it to triage.

Three Hundred Sixty Degree Feedback systems, while popular in business, have been less commonly implemented in local public health agencies. At the same time, they are effective methods of improving employee morale, work performance, organizational culture, and attainment of desired organizational outcomes. These systems can be purchased "off-the-shelf," or custom applications can be developed for a better fit with unique organizational needs. We describe the City of Milwaukee Health Department's successful experience customizing and implementing a 360-degree feedback system in the context of its ongoing total quality improvement efforts.

Hospital overcrowding and diversion of ambulances from emergency departments are being recognized as increasing problems in the health care system. This article, a descriptive narrative, examines the various factors contributing to the problem and describes how collaborative approaches to public health issues can be applied. It describes Milwaukee's experience with a collaborative approach. The use of a technological tool to assist with tracking and reporting on ambulance diversion and emergency department overload is explained, and data are provided to show the impact of various methods to blunt the impact of the flu season on diversion frequency. The article encourages use of similar collaborative approaches and Internet-based technology to address other public health problems.

Objectives. This study examined whether differences in heat alone, as opposed to public health interventions or other factors, accounted for the reduction in heat-related deaths and paramedic emergency medical service (EMS) runs between 1995 and 1999 during 2 heat waves occurring in Milwaukee, Wis. Methods. Two previously described prediction models were adapted to compare expected and observed heat-related morbidity and mortality in 1999 based on the city's 1995 experience. Results. Both models showed that heat-related deaths and EMS runs in 1999 were at least 49% lower than levels predicted by the 1995 relation between heat and heat-related deaths or EMS runs. Conclusions. Reductions in heat-related morbidity and mortality in 1999 were not attributable to differences in heat levels alone. Changes in public health preparedness and response may also have contributed to these reductions.

We have proposed needed information management capabilities for future US health departments predicated on trends in health care reform and health information technology. Regardless of whether health departments provide direct clinical services (and many will), they will manage unprecedented quantities of sensitive information for the public health core functions of assurance and assessment, including population-level health surveillance and metrics. Absent improved capabilities, health departments risk vestigial status, with consequences for vulnerable populations. Developments in electronic health records, interoperability and information exchange, public information sharing, decision support, and cloud technologies can support information management if health departments have appropriate capabilities. The need for national engagement in and consensus on these capabilities and their importance to health department sustainability make them appropriate for consideration in the context of accreditation.

Public health surveillance has benefitted from, and has often pioneered, informatics analyses and solutions. However, the field of informatics also serves other facets of public health including emergency response, environmental health, nursing, and administration. Public health informatics has been defined as the systematic application of information and computer science and technology to public health practice, research, and learning. It is an interdisciplinary profession that applies mathematics, engineering, information science, and related social sciences (e.g., decision analysis) to important public health problems and processes. Public health informatics is a subdomain of the larger field known as biomedical or health informatics. Health informatics is not synonymous with the term health information technology (IT). Although the concept of health IT encompasses the use of technology in the field of health care, one can think of health informatics as defining the science, the how and why, behind health IT. For example, health IT professionals should be able to resolve infrastructure problems with a network connection, whereas trained public health informaticians should be able to support public health decisions by facilitating the availability of timely, relevant, and high-quality information. In other words, they should always be able to provide advice on methods for achieving a public health goal faster, better, or at a lower cost by leveraging computer science, information science, or technology.

The Governor's Board for eHealth Care Quality and Patient Safety plans for universal adoption of electronic health records and information exchange.The inventory sought to describe characteristics, challenges, and policy recommendations of health information exchange (HIE) projects and create a directory and baseline for periodic reassessment.A cross-sectional Internet survey of any project where electronic patient information was transmitted by multiple organizations in Wisconsin in 2006.A description of operational and planned HIE projects, including stage of development, information users, organizational home, funding, governance, geographic scope, data standardization, drivers, internal and external challenges, and recommendations for statewide action.Twenty-one organizations sponsor 16 operational and 11 planned HIE projects. Most are surveillance programs, but a growing proportion serves clinicians and patients. Under half use data standards for interoperability. Leading internal challenges relate to funding, organizational and staff issues, governance, and technology. Leading external challenges are marketing, enlisting participants, regulatory issues, and sustainability.Wisconsin enjoys rich experience with HIE, but data remains largely in separate silos. Statewide collaboration, coordination and resource sharing can enhance the future of exchange efforts.

The Abbott LCx Neisseria gonorrhoeae assay (Abbott Laboratories, Abbott Park, Ill.) uses a ligase chain reaction (LCR) amplification in the LCx probe system for detection of a specific nucleotide sequence in the Opa-encoding gene of N. gonorrhoeae . We evaluated the LCx assay in a comparison with conventional culture employing modified Thayer-Martin media for the detection of N. gonorrhoeae from female endocervical specimens obtained from patients attending a sexually transmitted disease clinic. Discordantly LCR-positive and culture-negative specimens were further evaluated by testing with another LCR assay which used an N. gonorrhoeae -specific pilin probe. Specimens positive by both LCR assays were considered confirmed LCx-positive specimens. A specimen was considered to contain N. gonorrhoeae when it was either culture positive or culture negative and confirmed LCx positive. A total of 403 female endocervical specimens were evaluated. The prevalence of N. gonorrhoeae in this population was 8.7%. The sensitivity and specificity of the LCx assay were 94.3 and 99.4%, and those of culture were 77.1 and 100%, respectively. The Abbott LCx assay is a rapid, sensitive method for detection of N. gonorrhoeae in female endocervical specimens.

Medicine is increasingly practiced through the application of information sciences. Medical informatics deals with optimal information use within bioinformatics, imaging, clinical, and population health domains. Population health informatics plays an important role in that it critically informs practice in each of the other domains. Proper functioning of health care systems requires an advanced health information network that supports clinical care, personal health management, population health, and research. But this infrastructure does not yet exist in the United States. A number of federal initiatives are underway to address this problem, including the development of a framework for a national health information network and funding for implementation. This network will be facilitated by federal leadership, but public and private partnerships, and state, regional, and local implementation and policy development will play a critical role. In this article, we describe several Wisconsin initiatives that are keys to developing a strategic framework and building the state's electronic health information infrastructure.

The City of Milwaukee Health Department piloted a short-term, near real-time syndromic surveillance and communication tool by using an existing secure regional Internet infrastructure. Voluntary, active syndromic case reporting by hospital Emergency Departments was combined with other data streams, including clinical laboratory reports of communicable disease, hospital emergency room diversions, ambulance runs, medical examiner reports of unusual or suspicious deaths, poison control and nursing hotline call volumes, and pharmacy over-the-counter sales. These data were aggregated into a "Surveillance Dashboard" format that was used to communicate community syndromic health trends to hospitals, Emergency Departments, and other providers using a secure Internet technology. Emergency Departments at 8 area hospitals reported a total of 314 cases meeting syndromic criteria from 26,888 patient encounters. Participants were satisfied with data entry and communications. All participating Emergency Departments received e-mail and text pager alerts sent by the Milwaukee Health Department. No unexplained findings or suggestions of an early outbreak were reported through syndrome surveillance for the 4-week duration of the project. Similar surveillance and communications systems could provide multiple benefits to Emergency Department workflow and management, as well as to public health and emergency response.

During May and June 2003, an outbreak of febrile illness with vesiculopustular eruptions occurred among persons in the midwestern United States who had had contact with ill pet prairie dogs obtained through a common distributor. Zoonotic transmission of a bacterial or viral pathogen was suspected.We reviewed medical records, conducted interviews and examinations, and collected blood and tissue samples for analysis from 11 patients and one prairie dog. Histopathological and electron-microscopical examinations, microbiologic cultures, and molecular assays were performed to identify the etiologic agent.The initial Wisconsin cases evaluated in this outbreak occurred in five males and six females ranging in age from 3 to 43 years. All patients reported having direct contact with ill prairie dogs before experiencing a febrile illness with skin eruptions. We found immunohistochemical or ultrastructural evidence of poxvirus infection in skin-lesion tissue from four patients. Monkeypox virus was recovered in cell cultures of seven samples from patients and from the prairie dog. The virus was identified by detection of monkeypox-specific DNA sequences in tissues or isolates from six patients and the prairie dog. Epidemiologic investigation suggested that the prairie dogs had been exposed to at least one species of rodent recently imported into the United States from West Africa.Our investigation documents the isolation and identification of monkeypox virus from humans in the Western Hemisphere. Infection of humans was associated with direct contact with ill prairie dogs that were being kept or sold as pets.

Lenert and Sundwall identify opportunities and challenges of the Meaningful Use (MUse) incentive programs that advance standardized electronic reporting to health departments at a time when there is limited funding to upgrade systems. We concur that cloud-based Platform as a Service (PaaS) is a possible remedy. However, we disagree with their conclusion that “the security risks inherent in BioSense 2.0’s public cloud implementation may make this effort better suited to a demonstration project than a national level biodefense system.”1 The Afghan version of Conflict Kitchen is Bolani Pazi (www.conflictkitchen.org). Graphic design by Brett Yasko. Architectural design by Pablo Garcia of POiNT. Printed with permission. BioSense 2.02 is a functional public health surveillance system, not a demonstration project, hosted in a highly secure Internet government cloud. Before going live in November 2011, the system was tested, certified, and accredited to meet requirements of the Federal Information Security Management Act and independent security stewards. BioSense 2.0 is a PaaS; offers Software as Service (SaaS) to public health departments; and leverages Public Health Information Network (PHIN) standards and services such as PHIN Messaging, as well as new protocols such as Direct. BioSense 2.0 offers a low-barrier and low-cost approach that health departments can elect to offer to health care providers for reporting syndromic surveillance information. It accommodates secure data sharing among jurisdictions or with the Centers for Disease Control and Prevention (CDC), data exchange standards, jurisdictional information ownership, data sharing agreements, security and privacy rules, and data retention. BioSense 2.0 is user designed and governed in partnership with state and local health agencies, health care providers at the local and federal level, and national associations representing public health officials.3-6 The data sharing model and community-driven governance create a sustainable surveillance ecosystem to maintain situation awareness from local to national levels. The replacement of expensive technological services with a cloud-computing platform has resulted in substantial savings, allowing increased funding for health departments. (More information is available at http://biosense2.org.) CDC continues to work with health departments to enhance their capacity to receive and use information from MUse-prompted expansion in reporting of immunization services, laboratory test results for conditions of public health concern, and syndromic surveillance.7 BioSense 2.0 provides a foundation of services suitable for a future public health platform while simultaneously supporting current MUse requirements.

The sudden emergence of 2009 H1N1 influenza in the spring of that year sparked a surge in visits to emergency departments in New York City and other communities. A larger, second wave of cases was anticipated the following autumn. To reduce a potential surge of health system utilization without denying needed care, we enlisted the input of experts from medicine, public health, nursing, information technology, and other disciplines to design, test, and deploy clinical algorithms to help minimally trained health care workers and laypeople make informed decisions about care-seeking for influenza-like illness. The product of this collaboration, named Strategy for Off-Site Rapid Triage (SORT) was disseminated in 2 forms. Static algorithms, posted on the Centers for Disease Control and Prevention's Web site, offered guidance to clinicians and telephone call centers on how to manage adults and children with influenza-like illness. In addition, 2 interactive Web sites, http://www.Flu.gov and http://www.H1N1ResponseCenter.com, were created to help adults self-assess their condition and make an informed decision about their need for treatment. Although SORT was anchored in a previously validated clinical decision rule, incorporated the input of expert clinicians, and was subject to small-scale formative evaluations during rapid standup, prospective evaluation is lacking. If its utility and safety are confirmed, SORT may prove to be a useful tool to blunt health system surge and rapidly collect epidemiologic data on future disease outbreaks.