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We aimed to describe coronavirus disease 2019 (COVID-19) deaths among first responders early in the COVID-19 pandemic. We used media reports to gather timely information about COVID-19–related deaths among first responders during March 30–April 30, 2020, and evaluated the sensitivity of media scanning compared with traditional surveillance. We abstracted information about demographic characteristics, occupation, underlying conditions, and exposure source. Twelve of 19 US public health jurisdictions with data on reported deaths provided verification, and 7 jurisdictions reported whether additional deaths had occurred; we calculated the sensitivity of media scanning among these 7 jurisdictions. We identified 97 COVID-19–related first-responder deaths during the study period through media and jurisdiction reports. Participating jurisdictions reported 5 deaths not reported by the media. Sixty-six decedents worked in law enforcement, and 31 decedents worked in fire/emergency medical services. Media reports rarely noted underlying conditions. The media scan sensitivity was 88% (95% CI, 73%-96%) in the subset of 7 jurisdictions. Media reports demonstrated high sensitivity in documenting COVID-19–related deaths among first responders; however, information on risk factors was scarce. Routine collection of data on industry and occupation could improve understanding of COVID-19 morbidity and mortality among all workers.

As of September 21, 2020, the coronavirus disease 2019 (COVID-19) pandemic had resulted in 6,786,352 cases and 199,024 deaths in the United States.* Health care personnel (HCP) are essential workers at risk for exposure to patients or infectious materials (1). The impact of COVID-19 on U.S. HCP was first described using national case surveillance data in April 2020 (2). Since then, the number of reported HCP with COVID-19 has increased tenfold. This update describes demographic characteristics, underlying medical conditions, hospitalizations, and intensive care unit (ICU) admissions, stratified by vital status, among 100,570 HCP with COVID-19 reported to CDC during February 12-July 16, 2020. HCP occupation type and job setting are newly reported. HCP status was available for 571,708 (22%) of 2,633,585 cases reported to CDC. Most HCP with COVID-19 were female (79%), aged 16-44 years (57%), not hospitalized (92%), and lacked all 10 underlying medical conditions specified on the case report form† (56%). Of HCP with COVID-19, 641 died. Compared with nonfatal COVID-19 HCP cases, a higher percentage of fatal cases occurred in males (38% versus 22%), persons aged ≥65 years (44% versus 4%), non-Hispanic Asians (Asians) (20% versus 9%), non-Hispanic Blacks (Blacks) (32% versus 25%), and persons with any of the 10 underlying medical conditions specified on the case report form (92% versus 41%). From a subset of jurisdictions reporting occupation type or job setting for HCP with COVID-19, nurses were the most frequently identified single occupation type (30%), and nursing and residential care facilities were the most common job setting (67%). Ensuring access to personal protective equipment (PPE) and training, and practices such as universal use of face masks at work, wearing masks in the community, and observing social distancing remain critical strategies to protect HCP and those they serve.

During a pandemic, syndromic methods for monitoring illness outside of health care settings, such as tracking absenteeism trends in schools and workplaces, can be useful adjuncts to conventional disease reporting (1,2). Each month, CDC's National Institute for Occupational Safety and Health (NIOSH) monitors the prevalence of health-related workplace absenteeism among currently employed full-time workers in the United States, overall and by demographic and occupational subgroups, using data from the Current Population Survey (CPS).* This report describes trends in absenteeism during October 2019-April 2020, including March and April 2020, the period of rapidly accelerating transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). Overall, the prevalence of health-related workplace absenteeism in March and April 2020 were similar to their 5-year baselines. However, compared with occupation-specific baselines, absenteeism among workers in several occupational groups that define or contain essential critical infrastructure workforce† categories was significantly higher than expected in April. Significant increases in absenteeism were observed in personal care and service§ (includes child care workers and personal care aides); healthcare support¶; and production** (includes meat, poultry, and fish processing workers). Although health-related workplace absenteeism remained relatively unchanged or decreased in other groups, the increase in absenteeism among workers in occupational groups less able to avoid exposure to SARS-CoV-2 (3) highlights the potential impact of COVID-19 on the essential critical infrastructure workforce because of the risks and concerns of occupational transmission of SARS-CoV-2. More widespread and complete collection of occupational data in COVID-19 surveillance is required to fully understand workers' occupational risks and inform intervention strategies. Employers should follow available recommendations to protect workers' health.

During an influenza pandemic and during seasonal epidemics, more persons have symptomatic illness without seeking medical care than seek treatment at doctor's offices, clinics, and hospitals (1). Consequently, surveillance based on mortality, health care encounters, and laboratory data does not reflect the full extent of influenza morbidity. CDC uses a mathematical model to estimate the total number of influenza illnesses in the United States (1). In addition, syndromic methods for monitoring illness outside health care settings, such as tracking absenteeism trends in schools and workplaces, are important adjuncts to conventional disease reporting (2). Every month, CDC's National Institute for Occupational Safety and Health (NIOSH) monitors the prevalence of health-related workplace absenteeism among full-time workers in the United States using data from the Current Population Survey (CPS) (3). This report describes the results of workplace absenteeism surveillance analyses conducted during the high-severity 2017-18 influenza season (October 2017-September 2018) (4). Absenteeism increased sharply in November, peaked in January and, at its peak, was significantly higher than the average during the previous five seasons. Persons especially affected included male workers, workers aged 45-64 years, workers living in U.S. Department of Health and Human Services (HHS) Region 6* and Region 9,† and those working in management, business, and financial; installation, maintenance, and repair; and production and related occupations. Public health authorities and employers might consider results from relevant absenteeism surveillance analyses when developing prevention messages and in pandemic preparedness planning. The most effective ways to prevent influenza transmission in the workplace include vaccination and nonpharmaceutical interventions, such as staying home when sick, covering coughs and sneezes, washing hands frequently, and routinely cleaning frequently touched surfaces (5).

Background Transit workers have jobs requiring close public contact for extended periods of time, placing them at increased risk for severe acute respiratory syndrome coronavirus 2 infection and more likely to have risk factors for coronavirus disease 2019 (COVID‐19)‐related complications. Collecting timely occupational data can help inform public health guidance for transit workers; however, it is difficult to collect during a public health emergency. We used nontraditional epidemiological surveillance methods to report demographics and job characteristics of transit workers reported to have died from COVID‐19. Methods We abstracted demographic and job characteristics from media scans on COVID‐19 related deaths and reviewed COVID‐19 memorial pages for the Amalgamated Transit Union (ATU) and Transport Workers Union (TWU). ATU and TWU provided a list of union members who died from COVID‐19 between March 1–July 7, 2020 and a total count of NYC metro area union members. Peer‐reviewed publications identified through a scientific literature search were used to compile comparison demographic statistics of NYC metro area transit workers. We analyzed and reported characteristics of ATU and TWU NYC metro area decedents. Results We identified 118 ATU and TWU NYC metro area transit worker COVID‐19 decedents with an incidence proportion of 0.3%. Most decedents were male (83%); median age was 58 years (range: 39–71). Median professional tenure was 20 years (range: 2–41 years). Operator (46%) was the most reported job classification. More than half of the decedents (57%) worked in positions associated with close public contact. Conclusion Data gathered through nontraditional epidemiological surveillance methods provided insight into risk factors among this workforce, demonstrating the need for mitigation plans for this workforce and informing transit worker COVID‐19 guidance as the pandemic progressed.

ObjectivesThe Flint Community Resilience Group (Flint, Michigan USA) and the Centers for Disease Control and Prevention (CDC; Atlanta, Georgia USA) assessed behavioral health concerns among community members to determine the impact of lead contamination of the Flint, Michigan water supply.MethodsA Community Assessment for Public Health Emergency Response (CASPER) was conducted from May 17 through May 19, 2016 using a multi-stage cluster sampling design to select households and individuals to interview.ResultsOne-half of households felt overlooked by decision makers. The majority of households self-reported that at least one member experienced more behavioral health concerns than usual. The prevalence of negative quality of life indicators and financial concerns in Flint was higher than previously reported in the Michigan 2012 and 2014 Behavioral Risk Factor Surveillance System (BRFSS) survey.ConclusionsThe following can be considered to guide recovery efforts in Flint: identifying additional resources for behavioral health interventions and conducting follow-up behavioral health assessments to evaluate changes in behavioral health concerns over time; considering the impact of household economic factors when implementing behavioral health interventions; and ensuring community involvement and engagement in recovery efforts to ease community stress and anxiety.FortenberryGZ, ReynoldsP, BurrerSL, Johnson-LawrenceV, WangA, SchnallA, PullinsP, KieszakS, BayleyegnT, WolkinA. Assessment of behavioral health concerns in the community affected by the Flint water crisis — Michigan (USA) 2016. Prehosp Disaster Med. 2018;33(3):256–265.

Objective To describe the methodology of the National Institute for Occupational Safety and Health (NIOSH) system for national surveillance of health-related workplace absenteeism among full-time workers in the United States and to present initial findings from October through July of the 2017–2018 influenza season. Introduction During an influenza pandemic, when hospitals and doctors’ offices are—or are perceived to be—overwhelmed, many ill people may not seek medical care. People may also avoid medical facilities due to fear of contracting influenza or transmitting it to others. Therefore, syndromic methods for monitoring illness outside of health care settings are important adjuncts to traditional disease reporting. Monitoring absenteeism trends in schools and workplaces provide the archetypal examples for such approaches. NIOSH’s early experience with workplace absenteeism surveillance during the 2009–2010 H1N1 pandemic established that workplace absenteeism correlates well with the occurrence of influenza-like illness (ILI) and significant increases in absenteeism can signal concomitant peaks in disease activity. It also demonstrated that, while population-based absenteeism surveillance using nationally representative survey data is not as timely, it is more valid and reliable than surveillance based on data from sentinel worksites. 1 In 2017, NIOSH implemented population-based, monthly surveillance of health-related workplace absenteeism among full-time workers. Methods Each month, NIOSH updates an influenza season-based time series of health-related workplace absenteeism prevalence among full-time workers with the previous month’s estimate (i.e., with a 1-month lag). Data for this surveillance system come from the Current Population Survey (CPS), a monthly national survey of approximately 60,000 households administered by the Bureau of Labor Statistics. The CPS collects information on employment, demographics and other characteristics of the noninstitutionalized population aged 16 years or older. A full-time worker is defined as an employed person who reports that they usually work at least 35 hours per week. Health-related workplace absenteeism is defined as working fewer than 35 hours during the reference week due to the worker’s own illness, injury, or other medical issue. Because the CPS questions refer to one week of each month, absenteeism during the other weeks is not measured. These one-week measures are intended to be representative of all weeks of the month in which they occur. Monthly absenteeism prevalence estimates for the current influenza season are compared to an epidemic threshold defined as the 95% upper confidence limit of a baseline established using data from the previous five seasons aggregated by month. Point estimates that exceed the epidemic threshold signal surveillance warnings ; estimates whose lower 95% confidence limits exceed the epidemic threshold generate surveillance alerts . Estimates of total absenteeism are calculated as are estimates stratified by sex, age group, geographic region (HHS service regions), and occupation. All analyses are weighted using the CPS composite weight and estimates of all standard errors are adjusted to account for the complex design of the CPS sample. Results During the period October 2017 through July 2018, the prevalence of health-related workplace absenteeism among full-time workers began at 1.7% (95% CI 1.6–1.8%) in October, increased sharply beginning in November, peaked in January at 3.0% (95% CI 2.8–3.2%), and declined steadily thereafter to end at a low of 1.4% (95% CI 1.3–1.5%) in July. The January absenteeism peak significantly exceeded the epidemic threshold, signaling a surveillance alert. Absenteeism remained elevated in February, but not significantly, signaling a surveillance warning. (Figure 1) Peak absenteeism in the 2017-2018 influenza season exceeded that of all of the five previous seasons except the 2012-2013 season. (Figure 2) Analyses stratified by sex generated surveillance alerts for male workers in January and February. Surveillance alerts were also signaled for the following strata: workers aged 45–64 years in January and February; workers in HHS Region 6 in January and February and Region 9 in December and March; and workers in management, business, and financial occupations and installation, maintenance, and repair occupations in January and in production and related occupations in February. Unlike surveillance alerts, the numerous surveillance warnings generated in stratified analyses are not reported due to small sample sizes in several strata. Conclusions Results of initial analyses for the 2017–2018 influenza season indicate that, among full-time workers in the United States, the prevalence of health-related workplace absenteeism began to increase in November, peaked in January and was significantly higher than the average of the previous five seasons. These findings are consistent with official characterizations of 2017–2018, based on traditional ILI, hospitalization, and virologic surveillance data, as a high severity season that accelerated in November and peaked in January and February. 2,3 Analyses further suggest that male workers; workers aged 45–64 years; workers living in HHS Regions 6 and 9; and those working in management, business, and financial; installation, maintenance, and repair; and production and related occupations may have been especially impacted. While not timely enough to serve as an early warning system, population-based workplace absenteeism is, nevertheless, a useful syndromic measure of a pandemic’s impact on the working population. It also provides information that can be used to maintain health situational awareness during the inter-pandemic period, to evaluate the impact of pandemic control measures, and to inform future pandemic preparedness and response planning. Absenteeism surveillance can provide an important supplementary measure of a pandemic’s overall impact because morbidity and mortality statistics may not fully reflect the disruption caused to the social and economic life of the community. This is especially true when disease makes people too sick to work but not sick enough to seek medical care. References 1. Groenewold MR, Konicki DL, Luckhaupt SE, Gomaa A, Koonin LM. Exploring national surveillance for health-related workplace absenteeism: Lessons learned from the 2009 Influenza Pandemic. Disaster Med Public Health Preparedness. 2013;7:160–166. 2. Garten R, Blanton L, Elal AI, et al. Update: Influenza Activity in the United States During the 2017–18 Season and Composition of the 2018–19 Influenza Vaccine. MMWR Morb Mortal Wkly Rep 2018;67:634–642. 3. World Health Organization. Review of the 2017–2018 influenza season in the northern hemisphere. Wkly Epidemiol Rec. 2018;34(93):429–444.

Objectives: In January 2014, 4-methylcyclohexanemethanol spilled into the Elk River near Charleston, West Virginia, contaminating the water supply for about 120 000 households. The West Virginia American Water Company (WVAWC) issued a “do not use” water order for 9 counties. After the order was lifted (10 days after the spill), the communities’ use of public water systems, information sources, alternative sources of water, and perceived impact of the spill on households were unclear to public health officials. To assist in recovery efforts, the West Virginia Bureau for Public Health and the Centers for Disease Control and Prevention conducted a Community Assessment for Public Health Emergency Response (CASPER). Methods: We used the CASPER 2-stage cluster sampling design to select a representative sample of households to interview, and we conducted interviews in 171 households in April 2014. We used a weighted cluster analysis to generate population estimates in the sampling frame. Results: Before the spill, 74.4% of households did not have a 3-day alternative water supply for each household member and pet. Although 83.6% of households obtained an alternative water source within 1 day of the “do not use” order, 37.4% of households reportedly used WVAWC water for any purpose. Nearly 3 months after the spill, 36.1% of households believed that their WVAWC water was safe, and 33.5% reported using their household water for drinking. Conclusions: CASPER results identified the need to focus on basic public health messaging and household preparedness efforts. Recommendations included (1) encouraging households to maintain a 3-day emergency water supply, (2) identifying additional alternative sources of water for future emergencies, and (3) increasing community education to address ongoing concerns about water.

IntroductionCommunity Assessment for Public Health Emergency Response (CASPER) is an epidemiologic technique designed to provide quick, inexpensive, accurate, and reliable household-based public health information about a community’s emergency response needs. The Health Studies Branch at the Centers for Disease Control and Prevention (CDC) provides in-field assistance and technical support to state, local, tribal, and territorial (SLTT) health departments in conducting CASPERs during a disaster response and in non-emergency settings. Data from CASPERs conducted from 2003 through 2012 were reviewed to describe uses of CASPER, ascertain strengths of the CASPER methodology, and highlight significant findings.MethodsThrough an assessment of the CDC’s CASPER metadatabase, all CASPERs that involved CDC support performed in US states and territories from 2003 through 2012 were reviewed and compared descriptively for differences in geographic distribution, sampling methodology, mapping tool, assessment settings, and result and action taken by decision makers.ResultsFor the study period, 53 CASPERs were conducted in 13 states and one US territory. Among the 53 CASPERS, 38 (71.6%) used the traditional 2-stage cluster sampling methodology, 10 (18.8%) used a 3-stage cluster sampling, and two (3.7%) used a simple random sampling methodology. Among the CASPERs, 37 (69.9%) were conducted in response to specific natural or human-induced disasters, including 14 (37.8%) for hurricanes. The remaining 16 (30.1%) CASPERS were conducted in non-disaster settings to assess household preparedness levels or potential effects of a proposed plan or program. The most common recommendations resulting from a disaster-related CASPER were to educate the community on available resources (27; 72.9%) and provide services (18; 48.6%) such as debris removals and refills of medications. In preparedness CASPERs, the most common recommendations were to educate the community in disaster preparedness (5; 31.2%) and to revise or improve preparedness plans (5; 31.2%). Twenty-five (47.1%) CASPERs documented on the report or publications the public health action has taken based on the result or recommendations. Findings from 27 (50.9%) of the CASPERs conducted with CDC assistance were published in peer-reviewed journals or elsewhere.ConclusionThe number of CASPERs conducted with CDC assistance has increased and diversified over the past decade. The CASPERs’ results and recommendations supported the public health decisions that benefitted the community. Overall, the findings suggest that the CASPER is a useful tool for collecting household-level disaster preparedness and response data and generating information to support public health action.BayleyegnTM, SchnallAH, BallouSG, ZaneDF, BurrerSL, NoeRS, WolkinAF. Use of Community Assessments for Public Health Emergency Response (CASPERs) to rapidly assess public health issues — United States, 2003-2012. Prehosp Disaster Med. 2015;30(4):1-8.

Objective Electronic laboratory reporting has been promoted as a public health priority. The Office of the U.S. National Coordinator for Health Information Technology has endorsed two coding systems: Logical Observation Identifiers Names and Codes (LOINC) for laboratory test orders and Systemized Nomenclature of Medicine-Clinical Terms (SNOMED CT) for test results. Materials and Methods We examined LOINC and SNOMED CT code use in electronic laboratory data reported in 2011 by 63 non-federal hospitals to BioSense electronic syndromic surveillance system. We analyzed the frequencies, characteristics, and code concepts of test orders and results. Results A total of 14,028,774 laboratory test orders or results were reported. No test orders used SNOMED CT codes. To describe test orders, 77% used a LOINC code, 17% had no value, and 6% had a non-informative value, “OTH”. Thirty-three percent (33%) of test results had missing or non-informative codes. For test results with at least one informative value, 91.8% had only LOINC codes, 0.7% had only SNOMED codes, and 7.4% had both. Of 108 SNOMED CT codes reported without LOINC codes, 45% could be matched to at least one LOINC code. Conclusion Missing or non-informative codes comprised almost a quarter of laboratory test orders and a third of test results reported to BioSense by non-federal hospitals. Use of LOINC codes for laboratory test results was more common than use of SNOMED CT. Complete and standardized coding could improve the usefulness of laboratory data for public health surveillance and response.

Interferon-γ release assays (IGRAs) are an important tool for detecting latent Mycobacterium tuberculosis infection (LTBI). Insufficient data exist about IGRA specificity in college health centers, most of which screen students for LTBI using the tuberculin skin test (TST).Students at a low-TB incidence college health center.TST and T-SPOT.TB were performed on prospectively recruited students. TB exposure risk was assessed using a questionnaire: Those at low risk were assumed to not have LTBI in order to calculate test specificity.Of 184 students enrolled, 143 had results available for both TST and T-SPOT.TB. Agreement of the tests was 97% (kappa statistic 0.717; 95% confidence interval, 0.399-1.00). Among 124 low-risk students, specificity for TST and T-SPOT.TB were 98.4% and 100%, respectively.T-SPOT.TB specificity was high among low-risk students. Additional studies such as cost-effectiveness analyses using T-SPOT.TB as a single or confirmatory test to TST are needed to contribute to LTBI screening policy decisions.

All regions of the US experience disasters which result in a number of negative public health consequences. Some populations have higher levels of social vulnerability and, thus, are more likely to experience negative impacts of disasters including emotional distress, loss of property, illness, and death. To mitigate the impact of disasters on at-risk populations, emergency managers must be aware of the social vulnerabilities within their community. This paper describes a qualitative study which aimed to understand how emergency managers identify social vulnerabilities, also referred to as at-risk populations, in their populations and barriers and facilitators to current approaches. Findings suggest that although public health tools have been developed to aid emergency managers in identifying at-risk populations, they are not being used consistently. Emergency managers requested more information on the availability of tools as well as guidance on how to increase ability to identify at-risk populations.

Objective The objective was to use syndromic surveillance data from the North Carolina Disease Event Tracking and Epidemiologic Collection Tool NCDETECT and from BioSense to quantify the burden on North Carolina (NC) emergency departments of oral health-related visits more appropriate for care in a dental office (ED). Calculations were sought in terms of the Medicaid-covered visit rate relative to the Medicaid-eligible population by age group and by county. Introduction Concern over oral health-related ED visits stems from the increasing number of unemployed and uninsured, the cost burden of these visits, and the unavailability of indicated dental care in EDs [1]. Of particular interest to NC state public health planners are Medicaid-covered visits. Syndromic data in biosurveillance systems offer a means to quantify these visits overall and by county and age group. Methods Using BioSense data received from NCDETECT, 60.8 million records from 12.9 million ED visits were collected, covering all NC visits for state fiscal years (SFY) 2008–2010. Roughly 4% of visits were dropped because of patient residence zip codes missing or outside NC. A careful multi-step procedure involving both dentist consultants and data analysis was used to derive classification criteria for visits whose main reason was a nontraumatic oral health problem [2]. This procedure yielded 243,970 visits by ∼174,600 patients based on hospital-specific patient identifiers. Nontraumatic oral health-related visits were collected in a study set with added fields for method of payment, patient residence county, and age group. Based on previous studies, consultant preferences, and NC Medicaid eligibility guidelines, selected age groups were 0–14, 15–19, 20–29, 30–49, 50+ years. Stratified counts of Medicaid-eligibles were obtained from the NC Dental Director by study year. Using these tables and the ED visit study set, rates of nontraumatic oral health-related Medicaid visits per 10,000 eligibles were tabulated by county and age group for each study year. Demographics of multiple-visit patients were also studied. Results Rates of ED oral health-related visits were substantially higher for young adults than for other age groups. From statewide rates in Table 1, this age factor was consistent across study years. County-level rates showed the same age pattern to varying degrees. Detailed analysis showed problem areas, with rates in 21 of 100 counties exceeding 60 per 10,000 eligibles for the 20–29 year age group. Plots and tables complemented understanding of the ED oral health visit burden by age and county. The state total ED burden for oral health problems was ∼2% (0.2% – 9.7% by county). Conclusions Judicious use of syndromic data with external information, such as the detailed Medicaid denominators and the Method of Payment codes for each visit above, can give quantified estimates for policy-related public health issues. In the current study, the derived oral health visit rates gave numerical detail to concerns about the use of NC EDs for nontraumatic oral health problems by low-income persons affected by the economic recession. Results also show rate variation by county and can be combined with access-to-care data to inform planning of effective local measures to improve access to dental services and thus reduce the ED visit burden.

Electronic Laboratory Reporting (ELR) has the potential to be more accurate, timely, and cost-effective. However, the continuing use of non-standard, local codes to represent laboratory test results complicates the use of ELR data in public health practice. Use of structured and standardized coding system(s) to support the concepts represented by local codes improves the computational characteristics of ELR data. We examined the use of LOINC and SNOMED CT codes for coding laboratory tests in hospital laboratory reports. We found that the hospitals more frequently used LOINC codes than SNOMED CT in reporting test results.

Objective To develop a nontraumatic oral health classification that could estimate the burden of oral health-related visits in North Carolina (NC) Emergency Departments (EDs) using syndromic surveillance system data. Introduction Lack of access to regular dental care often results in costly, oral health visits to EDs that could otherwise have been prevented or managed by a dentist (1). Most studies on oral health-related visits to EDs have used a wide range of classifications from different databases, but none have used syndromic surveillance data. The volume, frequency, and included details of syndromic data enabled timely burden estimates of nontraumatic oral health visits for NC EDs. Methods Literature review, input by subject matter experts (SMEs), and analysis of syndromic data was used to create the nontraumatic oral health classification. BioSense, a near real-time, national-level, electronic health surveillance system was the source of the NC ED syndromic data. Visits with at least one oral health-related ICD-9-CM code were extracted for NC fiscal years 2008–2010. Univariate analyses of chief complaint (CC) and final diagnosis data along with SME consultation were used to determine the CC substrings and ‘white list’ of ICD-9-CM codes used as inclusion criteria to classify visits as oral health-related. These analyses and consultations also determined the trauma-related codes and substrings used to exclude visits. Results Table 1 shows all nontraumatic oral health-related ICD-9-CM codes used for the characterization. Codes likely related to the types of dental emergencies that routine dental care could not have prevented were excluded. Approximately 275,000 patient records were evaluated to determine the CC substrings. The final CC substrings chosen (Table 1) represented over 56% of visits in the candidate record dataset. Over 334,000 BioSense patient records were evaluated, and SMEs reviewed the 32 ICD-9-CM codes that co-occurred most commonly in visits containing oral health-related ICD-9-CM codes to determine which co-occurring ICD-9-CM codes (white list, Table 1) could be present and still maintain the main reason for the visit as an oral health-related problem. Trauma-related visit criteria used for exclusion were derived from a subset of BioSense sub-syndromes (Falls; Fractures and dislocation; Injury, NOS; Sprains and strains; and Motor vehicle traffic accidents) and from selected CC substrings (‘assault’, ‘fight’, and ‘brawl’). In summary, an ED visit had a nontraumatic oral health classification if it contained 1) an oral health-related CC substring with no trauma-related ICD-9-CM codes or CC substrings or 2) an oral health-related ICD-9 code accompanied by no oral health-related or trauma-related CC substrings and with no other diagnosis codes except for those on the whitelist. Conclusions There is increasing demand to determine ways to use syndromic surveillance data in an alternative way for population health surveillance. This use of BioSense data provided a practical classification of patient records for the tracking of nontraumatic oral health-related visits to NC EDs. Visit estimates created using this classification in combination with other pertinent information could prove useful to policymakers when deciding upon resource allocation aimed at reducing this unnecessary burden on the NC ED system. The large volume of records in syndromic surveillance systems offers substantial weight of evidence for alternative use in epidemiological studies; however, accurate classification of records is required to select cases of interest. While data volume precludes validation of every included record, a combination of human expertise and data analysis can provide credible classification criteria.

Background On 8 October 2008, members of a tour group experienced diarrhea and vomiting throughout an airplane flight from Boston, Massachusetts, to Los Angeles, California, resulting in an emergency diversion 3 h after takeoff. An investigation was conducted to determine the cause of the outbreak, assess whether transmission occurred on the airplane, and describe risk factors for transmission. Methods Passengers and crew were contacted to obtain information about demographics, symptoms, locations on the airplane, and possible risk factors for transmission. Case patients were defined as passengers with vomiting or diarrhea (> or =3 loose stools in 24 h) and were asked to submit stool samples for norovirus testing by real-time reverse-transcription polymerase chain reaction. Results Thirty-six (88%) of 41 tour group members were interviewed, and 15 (41%) met the case definition (peak date of illness onset, 8 October 2008). Of 106 passengers who were not tour group members, 85 (80%) were interviewed, and 7 (8%) met the case definition after the flight (peak date of illness onset, 10 October 2008). Multivariate logistic regression analysis showed that sitting in an aisle seat (adjusted relative risk, 11.0; 95% confidence interval, 1.4-84.9) and sitting near any tour group member (adjusted relative risk, 7.5; 95% confidence interval, 1.7-33.6) were associated with the development of illness. Norovirus genotype II was detected by reverse-transcription polymerase chain reaction in stool samples from case patients in both groups. Conclusions Despite the short duration, transmission of norovirus likely occurred during the flight.