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Objectives Syndromic surveillance can be used to enhance notifiable disease case-based surveillance. We analyzed features of varicella reported in Georgia to evaluate case detection through syndromic surveillance and to compare varicella reported through syndromic surveillance with varicella reported from all other sources. Methods Syndromic surveillance was incorporated into case-based varicella surveillance by the Georgia Department of Public Health (GDPH) in May 2016. A cross-sectional study design evaluated syndromic and nonsyndromic varicella reported to GDPH from May 1, 2016, through December 31, 2019. Varicella was reported by nonsyndromic sources including health care providers, schools, and laboratories. We identified syndromic varicella cases from urgent care and emergency department visit data with discharge diagnoses containing the terms “varicella” or “chickenpox.” Results Syndromic notifications accounted for 589 of 2665 (22.1%) suspected varicella reports investigated by GDPH. The positive predictive value was 33.1% for syndromic notifications and 31.3% for nonsyndromic notifications. Mean days from rash onset to GDPH notification was 3.2 days fewer ( P < .001) among patients identified through syndromic notification than among patients identified through nonsyndromic notification. The odds of varicella identified by syndromic notification being outbreak-associated were 0.18 (95% CI, 0.09-0.36) times those of varicella identified through nonsyndromic notification. Practice Implications Syndromic notifications were an effective, timely means for varicella case detection. Syndromic patients were significantly less likely than nonsyndromic patients to be outbreak-associated, possibly because of early detection. Syndromic surveillance enhanced case-based reporting for varicella in Georgia and was a useful tool to improve notifiable disease surveillance.

Background Reported coronavirus disease 2019 (COVID-19) cases underestimate true severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Data on all infections, including asymptomatic infections, are needed. To minimize biases in estimates from reported cases and seroprevalence surveys, we conducted a household-based probability survey and estimated cumulative incidence of SARS-CoV-2 infections adjusted for antibody waning. Methods From August to December 2020, we mailed specimen collection kits (nasal swabs and blood spots) to a random sample of Georgia addresses. One household adult completed a survey and returned specimens for virus and antibody testing. We estimated cumulative incidence of SARS-CoV-2 infections adjusted for waning antibodies, reported fraction, and infection fatality ratio (IFR). Differences in seropositivity among demographic, geographic, and clinical subgroups were explored with weighted prevalence ratios (PR). Results Among 1370 participants, adjusted cumulative incidence of SARS-CoV-2 was 16.1% (95% credible interval [CrI], 13.5%–19.2%) as of 16 November 2020. The reported fraction was 26.6% and IFR was 0.78%. Non-Hispanic black (PR, 2.03; 95% confidence interval [CI], 1.0–4.1) and Hispanic adults (PR, 1.98; 95% CI, .74–5.31) were more likely than non-Hispanic white adults to be seropositive. Conclusions As of mid-November 2020, 1 in 6 adults in Georgia had been infected with SARS-CoV-2. The COVID-19 epidemic in Georgia is likely substantially underestimated by reported cases., Using data from a probability survey of households in Georgia, United States we estimated that 1.3 million adults aged ≥18 years experienced SARS-CoV-2 infections by 16 November 2020, of whom 1 in 4 were reported and of whom 0.78% died.

To meet the educational, physical, social, and emotional needs of children, many U.S. schools opened for in-person learning during fall 2020 by implementing strategies to prevent transmission of SARS-CoV-2, the virus that causes COVID-19 (1,2). To date, there have been no U.S. studies comparing COVID-19 incidence in schools that varied in implementing recommended prevention strategies, including mask requirements and ventilation improvements* (2). Using data from Georgia kindergarten through grade 5 (K-5) schools that opened for in-person learning during fall 2020, CDC and the Georgia Department of Public Health (GDPH) assessed the impact of school-level prevention strategies on incidence of COVID-19 among students and staff members before the availability of COVID-19 vaccines.† Among 169 K-5 schools that participated in a survey on prevention strategies and reported COVID-19 cases during November 16-December 11, 2020, COVID-19 incidence was 3.08 cases among students and staff members per 500 enrolled students.§ Adjusting for county-level incidence, COVID-19 incidence was 37% lower in schools that required teachers and staff members to use masks, and 39% lower in schools that improved ventilation, compared with schools that did not use these prevention strategies. Ventilation strategies associated with lower school incidence included methods to dilute airborne particles alone by opening windows, opening doors, or using fans (35% lower incidence), or in combination with methods to filter airborne particles with high-efficiency particulate absorbing (HEPA) filtration with or without purification with ultraviolet germicidal irradiation (UVGI) (48% lower incidence). Multiple strategies should be implemented to prevent transmission of SARS-CoV-2 in schools (2); mask requirements for teachers and staff members and improved ventilation are important strategies that elementary schools could implement as part of a multicomponent approach to provide safer, in-person learning environments. Universal and correct mask use is still recommended by CDC for adults and children in schools regardless of vaccination status (2).

Background To inform prevention strategies, we assessed the extent of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and settings in which transmission occurred in a Georgia public school district. Methods During 1 December 2020–22 January 2021, SARS-CoV-2–infected index cases and their close contacts in schools were identified by school and public health officials. For in-school contacts, we assessed symptoms and offered SARS-CoV-2 reverse-transcription polymerase chain reaction (RT-PCR) testing; performed epidemiologic investigations and whole-genome sequencing to identify in-school transmission; and calculated secondary attack rate (SAR) by school setting (eg, sports, elementary school classroom), index case role (ie, staff, student), and index case symptomatic status. Results We identified 86 index cases and 1119 contacts, 688 (61.5%) of whom received testing. Fifty-nine of 679 (8.7%) contacts tested positive; 15 of 86 (17.4%) index cases resulted in ≥2 positive contacts. Among 55 persons testing positive with available symptom data, 31 (56.4%) were asymptomatic. Highest SARs were in indoor, high-contact sports settings (23.8% [95% confidence interval {CI}, 12.7%–33.3%]), staff meetings/lunches (18.2% [95% CI, 4.5%–31.8%]), and elementary school classrooms (9.5% [95% CI, 6.5%–12.5%]). The SAR was higher for staff (13.1% [95% CI, 9.0%–17.2%]) vs student index cases (5.8% [95% CI, 3.6%–8.0%]) and for symptomatic (10.9% [95% CI, 8.1%–13.9%]) vs asymptomatic index cases (3.0% [95% CI, 1.0%–5.5%]). Conclusions Indoor sports may pose a risk to the safe operation of in-person learning. Preventing infection in staff members, through measures that include coronavirus disease 2019 vaccination, is critical to reducing in-school transmission. Because many positive contacts were asymptomatic, contact tracing should be paired with testing, regardless of symptoms.

Psittacosis is typically a mild febrile respiratory illness caused by infection with the bacterium Chlamydia psittaci and usually transmitted to humans by infected birds (1). On average, 11 psittacosis cases per year were reported in the United States during 2000-2017. During August-October 2018, the largest U.S. psittacosis outbreak in 30 years (82 cases identified*) occurred in two poultry slaughter plants, one each in Virginia and Georgia, that shared source farms (2). CDC used C. psittaci real-time polymerase chain reaction (PCR) to test 54 human specimens from this outbreak. This was the largest number of human specimens from a single outbreak ever tested for C. psittaci using real-time PCR, which is faster and more sensitive than commercially available serologic tests. This represented a rare opportunity to assess the utility of multiple specimen types for real-time PCR detection of C. psittaci. C. psittaci was detected more frequently in lower respiratory specimens (59% [10 of 17]) and stool (four of five) than in upper respiratory specimens (7% [two of 28]). Among six patients with sputum and nasopharyngeal swabs tested, C. psittaci was detected only in sputum in five patients. Cycle threshold (Ct) values suggested bacterial load was higher in lower respiratory specimens than in nasopharyngeal swabs. These findings support prioritizing lower respiratory specimens for real-time PCR detection of C. psittaci. Stool specimens might also have utility for diagnosis of psittacosis.

We compared the characteristics of hospitalized and nonhospitalized patients who had coronavirus disease in Atlanta, Georgia, USA. We found that risk for hospitalization increased with a patient's age and number of concurrent conditions. We also found a potential association between hospitalization and high hemoglobin A1c levels in persons with diabetes.

Zika virus diagnostic testing and laboratory research increased considerably when Zika virus began spreading through the Americas in 2015, increasing the risk for potential Zika virus exposure of laboratory workers and biomedical researchers. We report 4 cases of laboratory-associated Zika virus disease in the United States during 2016-2019. Of these, 2 were associated with needlestick injuries; for the other 2 cases, the route of transmission was undetermined. In laboratories in which work with Zika virus is performed, good laboratory biosafety practices must be implemented and practiced to reduce the risk for infection among laboratory personnel.

We compared severe acute respiratory syndrome coronavirus 2 seroprevalence estimated from commercial laboratory residual sera and a community household survey in metropolitan Atlanta during April and May 2020 and found these 2 estimates to be similar (4.94% vs 3.18%). Compared with more representative surveys, commercial sera can provide an approximate measure of seroprevalence.

Background Coronavirus disease (COVID-19) can cause severe illness and death. Predictors of poor outcome collected on hospital admission may inform clinical and public health decisions. Methods We conducted a retrospective observational cohort investigation of 297 adults admitted to 8 academic and community hospitals in Georgia, United States, during March 2020. Using standardized medical record abstraction, we collected data on predictors including admission demographics, underlying medical conditions, outpatient antihypertensive medications, recorded symptoms, vital signs, radiographic findings, and laboratory values. We used random forest models to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for predictors of invasive mechanical ventilation (IMV) and death. Results Compared with age Conclusions After adjustment for patient and clinical characteristics, older age was the strongest predictor of death, exceeding comorbidities, abnormal vital signs, and laboratory test abnormalities. That coronary artery disease, but not chronic lung disease, was associated with death among hospitalized patients warrants further investigation, as do associations between certain antihypertensive medications and death.

SARS-CoV-2, the novel coronavirus that causes coronavirus disease 2019 (COVID-19), was first detected in the United States during January 2020 (1). Since then, >980,000 cases have been reported in the United States, including >55,000 associated deaths as of April 28, 2020 (2). Detailed data on demographic characteristics, underlying medical conditions, and clinical outcomes for persons hospitalized with COVID-19 are needed to inform prevention strategies and community-specific intervention messages. For this report, CDC, the Georgia Department of Public Health, and eight Georgia hospitals (seven in metropolitan Atlanta and one in southern Georgia) summarized medical record-abstracted data for hospitalized adult patients with laboratory-confirmed* COVID-19 who were admitted during March 2020. Among 305 hospitalized patients with COVID-19, 61.6% were aged

Previous reports of COVID-19 case, hospitalization, and death rates by vaccination status

BACKGROUND: With the emergence of the delta variant, the United States experienced a rapid increase in Covid-19 cases in 2021. We estimated the risk of breakthrough infection and death by month of vaccination as a proxy for waning immunity during a period of delta variant predominance. METHODS: Covid-19 case and death data from 15 U.S. jurisdictions during January 3 to September 4, 2021 were used to estimate weekly hazard rates among fully vaccinated persons, stratified by age group and vaccine product. Case and death rates during August 1 to September 4, 2021 were presented across four cohorts defined by month of vaccination. Poisson models were used to estimate adjusted rate ratios comparing the earlier cohorts to July rates. RESULTS: During August 1 to September 4, 2021, case rates per 100,000 person-weeks among all vaccine recipients for the January to February, March to April, May to June, and July cohorts were 168.8 (95% confidence interval [CI], 167.5 to 170.1), 123.5 (95% CI, 122.8 to 124.1), 83.6 (95% CI, 82.9 to 84.3), and 63.1 (95% CI, 61.6 to 64.6), respectively. Similar trends were observed by age group for BNT162b2 (Pfizer–BioNTech) and mRNA-1273 (Moderna) vaccine recipients. Rates for the Ad26.COV2.S (Janssen-Johnson & Johnson) vaccine were higher; however, trends were inconsistent. BNT162b2 vaccine recipients 65 years of age or older had higher death rates among those vaccinated earlier in the year. Protection against death was sustained for the mRNA-1273 vaccine recipients. Across age groups and vaccine types, people who were vaccinated 6 months ago or longer (January-February) were 3.44 (3.36 to 3.53) times more likely to be infected and 1.70 (1.29 to 2.23) times more likely to die from COVID-19 than people vaccinated recently in July 2021. CONCLUSIONS: Our study suggests that protection from SARS-CoV-2 infection among all ages or death among older adults waned with increasing time since vaccination during a period of delta predominance. These results add to the evidence base that supports U.S. booster recommendations, especially for older adults vaccinated with BNT162b2 and recipients of the Ad26.COV2.S vaccine. (Funded by the Centers for Disease Control and Prevention.)

Automated identification systems may misidentify Brucella, the causative agent of brucellosis, which may be re-emerging in the United States as the result of an expanding feral swine population. We present a case of Brucella suis likely associated with feral swine exposure that was misidentified as Ochrobactrum anthropi, a phylogenetic relative.

We used the BinaxNOW COVID-19 Ag Card to screen 1,540 asymptomatic college students for severe acute respiratory syndrome coronavirus 2 in a low-prevalence setting. Compared with reverse transcription PCR, BinaxNOW showed 20% overall sensitivity; among participants with culturable virus, sensitivity was 60%. BinaxNOW provides point-of-care screening but misses many infections.

There is increasing evidence that children and adolescents can efficiently transmit SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (1-3). During July-August 2020, four state health departments and CDC investigated a COVID-19 outbreak that occurred during a 3-week family gathering of five households in which an adolescent aged 13 years was the index and suspected primary patient; 11 subsequent cases occurred.

Limited data are available about transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), among youths. During June 17-20, an overnight camp in Georgia (camp A) held orientation for 138 trainees and 120 staff members; staff members remained for the first camp session, scheduled during June 21-27, and were joined by 363 campers and three senior staff members on June 21. Camp A adhered to the measures in Georgia's Executive Order* that allowed overnight camps to operate beginning on May 31, including requiring all trainees, staff members, and campers to provide documentation of a negative viral SARS-CoV-2 test ≤12 days before arriving. Camp A adopted most† components of CDC's Suggestions for Youth and Summer Camps§ to minimize the risk for SARS-CoV-2 introduction and transmission. Measures not implemented were cloth masks for campers and opening windows and doors for increased ventilation in buildings. Cloth masks were required for staff members. Camp attendees were cohorted by cabin and engaged in a variety of indoor and outdoor activities, including daily vigorous singing and cheering. On June 23, a teenage staff member left camp A after developing chills the previous evening. The staff member was tested and reported a positive test result for SARS-CoV-2 the following day (June 24). Camp A officials began sending campers home on June 24 and closed the camp on June 27. On June 25, the Georgia Department of Public Health (DPH) was notified and initiated an investigation. DPH recommended that all attendees be tested and self-quarantine, and isolate if they had a positive test result.

BackgroundReported COVID-19 cases underestimate the true number of SARS-CoV-2 infections. Data on all infections, including asymptomatic infection, are needed to guide state testing and prevention programs. To minimize biases in estimates from seroprevalence surveys and reported cases, we conducted a state-wide probability survey of Georgia households and estimated cumulative incidence of SARS-CoV-2 infections adjusted for antibody waning.MethodsFrom August to December 2020, we mailed kits to self-collect specimens (nasal swabs and blood spots) to a random sample of Georgia addresses. One randomly-selected adult household member completed a survey and returned specimens for virus and antibody testing. We estimated cumulative incidence of SARS-CoV-2 infections adjusted for waning antibodies, reported fraction, and infection fatality ratio (IFR). Differences in seropositivity among demographic, geographic and clinical subgroups were explored with weighted prevalence ratios (PR).ResultsAmong 1,370 Georgia adult participants, adjusted cumulative incidence of SARS-CoV-2 was 16.1% (95% credible interval (CrI): 13.5-19.2%) as of November 16, 2020. The reported fraction was 26.6% and IFR was 0.78%. Non-Hispanic Black (PR: 2.03, CI 1.0, 4.1) and Hispanic adults (PR: 1.98, CI 0.74, 5.31) were more likely than non-Hispanic White adults to be seropositive. Seropositivity in metropolitan Atlanta’s Fulton and DeKalb counties was similar to seropositivity elsewhere in Georgia (7.8% vs. 8.8%).ConclusionsAs of mid-November 2020, one in 6 adults in Georgia had been infected with SARS-CoV-2. The scope of the COVID-19 epidemic in Georgia is likely substantially underestimated by reported cases.Main pointUsing data from a probability survey of households in Georgia, USA, we estimated that 1.3 million adults aged ≥18 years experienced SARS-CoV-2 infections by November 16, 2020, of whom 1 in 4 were reported and of whom 0.78% died.

Background The epidemiological features and outcomes of hospitalized adults with coronavirus disease 2019 (COVID-19) have been described; however, the temporal progression and medical complications of disease among hospitalized patients require further study. Detailed descriptions of the natural history of COVID-19 among hospitalized patients are paramount to optimize health care resource utilization, and the detection of different clinical phenotypes may allow tailored clinical management strategies. Methods This was a retrospective cohort study of 305 adult patients hospitalized with COVID-19 in 8 academic and community hospitals. Patient characteristics included demographics, comorbidities, medication use, medical complications, intensive care utilization, and longitudinal vital sign and laboratory test values. We examined laboratory and vital sign trends by mortality status and length of stay. To identify clinical phenotypes, we calculated Gower’s dissimilarity matrix between each patient’s clinical characteristics and clustered similar patients using the partitioning around medoids algorithm. Results One phenotype of 6 identified was characterized by high mortality (49%), older age, male sex, elevated inflammatory markers, high prevalence of cardiovascular disease, and shock. Patients with this severe phenotype had significantly elevated peak C-reactive protein creatinine, D-dimer, and white blood cell count and lower minimum lymphocyte count compared with other phenotypes (P Conclusions Among a cohort of hospitalized adults, we identified a severe phenotype of COVID-19 based on the characteristics of its clinical course and poor prognosis. These findings need to be validated in other cohorts, as improved understanding of clinical phenotypes and risk factors for their development could help inform prognosis and tailored clinical management for COVID-19.

A better understanding of SARS-CoV-2 transmission from children and adolescents is crucial for informing public health mitigation strategies. We conducted a retrospective cohort study among household contacts of primary cases defined as children and adolescents aged 7⍰19 years with laboratory evidence of SARS-CoV-2 infection acquired during an overnight camp outbreak. Among household contacts, we defined secondary cases using the Council of State and Territorial Epidemiologists definition. Among 526 household contacts of 224 primary cases, 48 secondary cases were identified, corresponding to a secondary attack rate of 9% (95% confidence interval [CI], 7%–12%). Our findings show that children and adolescents can transmit SARS-CoV-2 to adult contacts and other children in a household setting.

Since February 12, 2020, approximately 6.5 million cases of SARS-CoV-2 infection, the cause of coronavirus disease 2019 (COVID-19), and 190,000 SARS-CoV-2-associated deaths have been reported in the United States (1,2). Symptoms associated with SARS-CoV-2 infection are milder in children compared with adults (3). Persons aged

During August–October, 2018, an outbreak of severe respiratory illness was reported among poultry slaughter plant workers in Virginia and Georgia, USA. A multiorganizational team investigated the cause and extent of illness, determined that the illness was psittacosis, and evaluated and recommended controls for health hazards in the workplace to prevent additional cases.

BACKGROUND Understanding how youth perceive eating healthy foods can inform programs and policies that aim to improve healthy eating. We assessed the reasons for and the most common locations of eating healthy foods among Georgia's (GA) high school (HS) students. METHODS Using the 2013 GA HS Youth Risk Behavior Survey, we examined motivations for and locations of eating healthy foods by sociodemographic characteristics and daily fruit and vegetable intake. Weighted chi-square tests were used to examine differences in responses for each perception. RESULTS Nineteen percent of GA HS students consumed fruit ≥3 times/day and 11% consumed vegetables ≥3 times/day. The most frequently chosen response to motivations for eating healthy foods was the desire to be healthy (42%), followed by enjoying their taste (18%). The most likely location to eat healthy foods was at home (80%), followed by at school (13%). CONCLUSIONS GA HS students are most motivated to eat healthy foods by the desire to be healthy and their enjoyment of the taste of healthy foods. Incorporating messages that align with students' motivations to eat healthy foods could be considered when developing promotional programs/policies targeting healthy eating.

Background: In April 2019, the Georgia Department of Public Health (DPH) initiated whole-genome sequencing (WGS) on NDM-producing Enterobacteriaceae identified since January 2018. The WGS data analyzed at CDC identified related Klebsiella pneumoniae isolates with hypervirulence markers from 2 patients. Carbapenemase-producing hypervirulent K. pneumoniae (CP-hvKP) are rarely reported in the United States, but they can to cause serious, highly resistant, invasive infections. We conducted an investigation to identify cases and prevent spread. Methods: We defined a case as NDM-producing K. pneumoniae with ≥4 hypervirulence markers identified by WGS, isolated from any specimen source from a Georgia patient. We reviewed the case patient’s medical history to identify potentially affected facilities. We also performed PCR-based colonization screening and retrospective and prospective laboratory-based surveillance. Finally, we assessed facility infection control practices. Results: Overall, 7 cases from 3 case patients (A, B, and C) were identified (Fig. 1). The index case specimen was collected from case-patient A at ventilator-capable skilled nursing facility 1 (vSNF1) in May 2018. Case-patient A had been hospitalized for 1 month in India before transfer to the United States. Case-patient B’s initial isolate was collected in January 2019 on admission to vSNF2 from a critical access hospital (CAH). The CAH laboratory retrospectively identified case-patient C, who overlapped with case-patient B at the CAH in October 2018. The CAH and the vSNF2 are geographically distant from vSNF1. Case-patients B and C had no known epidemiologic links to case-patient A. Colonization screening occurred at vSNF1 in May 2018, following detection of NDM-producing K. pneumoniae from case-patient A ∼1 year before determining that the isolate carried hypervirulence markers. Among 30 residents screened, 1 had NDM and several had other carbapenemases. Subsequent screening did not identify additional NDM. Colonization screening of 112 vSNF2 residents and 13 CAH patients in 2019 did not reveal additional case patients; case-patient B resided at vSNF2 at the time of screening and remained colonized. At all 3 facilities, the DPH assessed infection control practices, issued recommendations to resolve lapses, and monitored implementation. The DPH sequenced all 27 Georgia NDM–K. pneumoniae isolates identified since January 2018; all were different multilocus sequence types from the CP-hvKP isolates, and none possessed hypervirulence markers. Conclusions: We hypothesize that CP-hvKP was imported by a patient hospitalized in India and spread to 3 Georgia facilities in 2 distinct geographic regions through indirect patient transfers. Although a response to contain NDM at vSNF1 in 2018 likely limited CP-hvKP transmission, WGS identified hvKP and established the relatedness of isolates from distinct regions, thereby directing the DPH’s additional containment activities to halt transmission.Funding: NoneDisclosures: None

Objective Describe how the Georgia Department of Public Health (DPH) used data from its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) module for early detection of an outbreak of salmonellosis in Camden County, Georgia. Introduction Evidence about the value of syndromic surveillance data for outbreak detection is limited (1). In July 2018, a salmonellosis outbreak occurred following a family reunion of 300 persons held in Camden County, Georgia, where one meal was served on 7/27/2018 and on 7/28/2018. Methods SendSS-SS and SAS were used for cluster detection of Emergency Department (ED) patients with similar Chief Complaint (CC), Triage Notes (TN), or Discharge Diagnoses (DDx) by facility, time of ED visit, and zip code / county of residence. A SAS-based free-text query related to food poisoning in the CC and DDx fields was also performed on a daily basis. County- and hospital-specific charting of the Diarrhea syndrome was also conducted in SendSS-SS, whereas county- and zip code-specific charting of the same syndrome were done in both SendSS-SS and SAS (2). Results On Sunday July 29 th , 2018, three children and three adults were seen within 18 hours at the ED of Hospital A in Camden County, Georgia. All patients complained of diarrhea, vomiting, and food poisoning, after a large family reunion that had been held the day before. This early cluster was detected by the SAS-based free-text query of ‘ food poisoning ’ and the SAS-based cluster detection tool for patients with Diarrhea syndrome. The District Epidemiologists (DE) in the Coastal Health District were notified on Monday, July 30 th , 2018. One-year high daily spikes of the Diarrhea syndrome occurred from July 29 th to July 31 st , 2018 in a local hospital ED (Fig 1), Camden County, and zip code 31548. Two HIPAA-compliant line lists with a total of 27 patients seen at EDs were emailed to the DEs to support active case finding. No further spikes of the Diarrhea syndrome were detected in Camden County during the 2-week period after the 3-day spike. Conclusions Syndromic surveillance was a useful surveillance tool for early detection of a salmonellosis outbreak, helping with the active search for outbreak cases, tracking the peak of the outbreak, and assuring that no further spikes were occurring. References 1.R Hopkins, C Tong, H Burkom, et al . A Practitioner-Driven Research Agenda for Syndromic Surveillance. Public Health Reports 2017; 132(Supplement1): 116S-126S. 2. G Zhang, A Llau, J Suarez, E O'Connell, E Rico, R Borroto, F Leguen. Using ESSENCE to Track a Gastrointestinal Outbreak in a Homeless Shelter in Miami-Dade County, 2008. Advances in Disease Surveillance. 2008; 5:139.

Introduction: Acute stroke patients must be transported to stroke-ready facilities swiftly for better patient outcomes. Public knowledge of stroke signs and the need to call emergency medical services (EMS) is critical to ensure that stroke patients get to hospital timely. This study assesses the impact of a targeted stroke awareness campaign launched by the Georgia Department of Public Health (DPH) in 2017. Method: From May to September 2017, the Georgia DPH launched a multipronged public stroke education campaign involving public service announcements, posters on mass transport systems, billboards on highways, and digital displays at sport events. The campaign targeted African-Americans because of their higher risk for stroke. Four questions about stroke signs (facial droop, arm weakness and slurred speech) and the importance of calling 911 were added to the 2017 Georgia Behavioral Risk Factor Surveillance System (BRFSS) survey. The pre and post-campaign levels of knowledge of stroke signs, as determined by answers to BRFSS questions, were compared among different race groups using SAS-callable SUDDAN software and applying logistic regressions for model adjusted prevalence differences. Results: In general, a 3.8% to a 5.5% improvement in knowledge was documented for each sign of stroke. The proportion of adult Georgians who knew all three signs of stroke included in the campaign and the importance of calling 911 increased from 49.9% pre-campaign to 56.0% post-campaign. However, the improvement was significant and more pronounced among African Americans (16%, SE=7%) than among Whites (1%, SE=4%), and Hispanics (3%, SE=10%) controlling for other potential predictors. Moreover, relatively fewer Hispanics (29%; 95%CI: 19%, 41%) provided the correct answers to the four questions than African-Americans (58%; 95%CI: 51%, 64%) and Whites (62%, 95%CI: 58%, 67%) post-campaign. Conclusion: The Georgia DPH’s targeted mass educational campaign was effective in improving the knowledge of stroke signs among the public and the need to call EMS. The Department must continue its education effort to improve public’s knowledge with emphasis on reaching the Hispanic population.

On October 7, 2016, Hurricane Matthew traveled along the coasts of Florida, Georgia, and South Carolina causing flooding and power outages. The Georgia Department of Public Health (DPH) developed the Web-based Responder Safety, Tracking, and Resilience (R-STaR) system to monitor the health and safety of public health responders and to inform disaster response planning for Hurricane Matthew. Using R-STaR, responders (n = 126) were e-mailed a daily survey while deployed to document injuries or harmful exposures and a post-deployment survey on their post-deployment health and satisfaction with using R-STaR. DPH epidemiologists contacted responders reporting injuries or exposures to determine the need for medical care. Frequencies were tabulated for quantitative survey responses, and qualitative data were summarized into key themes. Five percent (6/126) of responders reported injuries, and 81% (43/53) found R-STaR easy to use. Suggestions for R-STaR improvement included improving accessibility using mobile platforms and conducting pre-event training of responders on R-STaR. Lessons learned from R-STaR development and evaluation can inform the development and improvement of responder health surveillance systems at other local and state health departments and disaster and emergency response agencies. (Disaster Med Public Health Preparedness. 2019;13:74–81).

Objective: To describe how the Georgia Department of Public Health (DPH) uses ICD-9 and ICD-10-based discharge diagnoses (DDx) codes assigned to Emergency Department (ED) patients to support the early detection and investigation of outbreaks, clusters, and individual cases of reportable diseases. Introduction: The Georgia DPH has used its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) module to collect, analyze and display analyses of ED patient visits, including DDx data from hospitals throughout Georgia for early detection and investigation of cases of reportable diseases before laboratory test results are available. Evidence on the value of syndromic surveillance approaches for outbreak or event detection is limited (1, 2). Use of the DDx field within datasets, specifically as it might be used for investigation of outbreaks, clusters, and / or individual cases of reportable diseases, has not been widely discussed. Methods: The DDx field of the ED data set sent to DPH by 120 facilities was queried for diseases that are immediately-reportable, as well as those reportable within 7 days of diagnosis. The query was performed twice a day using a combination of SAS 9.4 and the internal query capabilities of SendSS. District Epidemiologists (DE) were notified by email, with an Excel file attached that includes the details of the patient’s visit. DEs contacted Infection Control Practitioners (ICPs) of the facilities where the patients had received a discharge diagnosis of a reportable disease. True or false positives were determined after the outcome of the follow-up with the ICP had been known and after the patient had been entered as a case of reportable disease in SendSS by the DE. Hence, if the patient was a confirmed or probable case of a reportable disease, it was recorded as a True Positive, and True Negative otherwise. This led to the calculation of Predictive Value Positive (PVP) by reportable disease. Results: Table 1 shows the number of notifications sent to DEs that were later demonstrated to be True Positives and False Positives. It also shows the PVP by diseases being reported, for the period spanning from 05/01/2016 to 08/31/2017. Use of these notifications has allowed early investigation and identification of 158 cases of notifiable diseases by DEs. This includes 25 epi-linked cases (varicella=12, pertussis=4, cryptosporidiosis=3, shigellosis=2, malaria=2, and viral meningitis=2), as well as two clusters of varicella, one cluster of pertussis, and one outbreak of varicella in an elementary school that had not been reported to the local health department. A notable limitation of this study is that no systematic and exhaustive tracking is done of all notifications, as DEs have latitude to decide whether to follow up on a specific notification, depending on other local data that could be related to the event. Therefore, the PPVs may be biased due to over- / under-estimation of unknown size and direction. One exception to this is varicella notifications, whose outcomes have been exhaustively tracked by the DPH surveillance coordinator of this disease. Conclusions: The use of ED discharge diagnoses to examine potential cases of reportable diseases can help improve detection and early response by local health departments to outbreaks, clusters, and individual cases of reportable diseases. Exhaustive tracking of all the notifications by specific diseases may improve the estimation of the PPVs of the notifications sent to DEs.

Objective To better understand the importance of monitoring responders during public health emergencies and to learn how the Georgia Department of Public Health (DPH) developed and deployed an electronic responder monitoring tool. Introduction During an emergency, the state of Georgia depends on public health staff and volunteers to respond. It is imperative that staff are safe before, during and after deployment. Emergency response workers must be protected from the hazardous conditions that disasters and other emergencies create 1 . In October 2016 and September 2017, Hurricanes Matthew and Irma caused widespread evacuation of Georgia residents, initiating a tremendous sheltering effort. Hundreds of public health responders were deployed to assist with sheltering and other aspects of the response. DPH rapidly developed a novel electronic Responder Safety, Tracking and Resilience module, which was used to track public health responders and monitor their health and safety while deployed. Methods DPH rapidly developed a novel electronic Responder Safety, Tracking, and Resilience module (R-STaR), within the existing State Electronic Notifiable Disease Surveillance System to monitor the health and safety of responders. R-STaR was originally used during Hurricane Matthew, where it was launched the day of the storm, and was launched again four days before Hurricane Irma made landfall. Responders were emailed a web-based link to register, indicating demographic information, contact information, work location, subject area, vaccination status, and whether they considered themselves mentally and physically fit to deploy. Responders then received a daily email with a link to document their daily deployment location, duties, and whether they had any hazardous exposures, illness, or injuries while deployed. A post-deployment survey was sent to responders after Hurricane Matthew to solicit feedback about the responder safety module. Results During Hurricane Matthew, 128 responders representing 11 Georgia Public Health Districts registered in R-STaR. Seven responders reported illness or injury and were contacted to determine if medical services were needed; all remained healthy post-deployment. During Hurricane Irma, 1240 responders representing DPH and 16 Public Health Districts, and other employers, including law enforcement, fire, and education, registered in R-STaR. Of 472 responders completing daily health checks during their Irma deployment, 48 reported an injury, illness, or exposure, and were contacted to determine if services were needed. The daily health checks led to the identification of an outbreak of influenza in one of the shelters and resulted in vaccination or antiviral prophylaxis administration to 76 responders. Fifty responders to Hurricane Matthew completed the post-deployment survey; 95% found R-STaR easy to use, and 92% indicated that they liked being monitored. Supervisors indicated that the module could be used to: 1) roster and credential responders prior to an event; 2) track where responders are, monitor their health and safety during an event, and quantify the human resources deployed during a declared emergency; and, 3) to distribute post-response responder resources, monitor responder health, and gather information for after-action reports. Conclusions R-STaR was widely used and well received despite being implemented with no prior training, with a dramatic increase in the number of responders registering from the first implementation in 2016 to the second implementation in September 2017. Monitoring responder health and safety is crucial to responding to and preventing outbreaks during a response, and ensuring responders get appropriate mental and physical support after a deployment. Lessons learned from both events will be used to create a just-in-time training curriculum, and develop a more robust R-STaR, which will enable responder rostering, credentialing, tracking and monitoring before, during, and after an event to ensure the health and safety of our responders as well as for future planning. References 1. Centers for Disease Control and Prevention (2017). EMERGENCY RESPONDER HEALTH MONITORING AND SURVEILLANCE (ERHMS) . Retrieved from Centers for Disease Control and Prevention: https://www.cdc.gov/niosh/erhms/default.html.

Objective To explore the timeliness of emergency room surveillance data after the advent of federal Meaningful Use initiatives and determine potential areas for improvement. Introduction Timeliness of emergency room (ER) data is arguably its strongest attribute in terms of its contribution to disease surveillance. Timely data analyses may improve the efficacy of prevention and control measures. There are a number of studies that have looked at timeliness prior to the advent of Meaningful Use, and these studies note that ER data were not fast enough for them to be useful in real time 2,3 . However, the change in messaging practices in the Meaningful Use era potentially changes this. Other studies have shown that changes in processes and protocol can dramatically improve timeliness 1,4 and this motivates the current study of timeliness to identify processes that can be changed to improve timeliness. Methods ER data were collected from March 2017 through September 2017 from both the Georgia Department of Public Health’s (GDPH) State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance Module and the Centers for Disease Control and Prevention (CDC) National Syndromic Surveillance Program’s (NSSP) ESSENCE systems. Patients from hospitals missing 10 or more days of data, as well as patients with missing or invalid triage times, and all visits after August 1st were excluded in order to ensure data were representative of a “typical” time period and that a sufficient amount of time was given for visits to arrive from hospitals. The timeliness of individual records was determined in a number of different ways. All timeliness measurements were determined by subtracting the earlier time event from the later time of the event. The overall measure of timeliness is the time between the patient’s triage time and the data being present in the ESSENCE data system. In between, Georgia’s SendSS system receives and processes the data. This is illustrated in Figure 1. Due to the skewed nature of these measures, they were analyzed using medians and Gaussian kernel density plots. Results The study in total included records from 118 Georgia hospitals, 14,203 data files and 1,897,501 patient records. Overall median timeliness of data from Triage Time to being available in SendSS for analyses was 33.62 hours (IQR=28.5), and in ESSENCE was 45.08 hours (IQR=37.05). The distributions of Triage Time of Day, Time Available in SendSS Staging, and Time Available in ESSENCE Analysis can be seen in Figure 2. Additionally, lines were added for when SendSS makes data available for its own analyses and when it sends data to ESSENCE. These latter lines represent places where the SendSS system itself could improve, and potential improved times were noted based on the kernel densities. Peak triage times for Georgia hospitals were between 10 am to 11 pm, shown in black. This represents the ideal timeliness if Hospitals sent their data immediately. However, data was all batched by Georgia hospitals and sent at different times of the day. The distribution of the time patient records arrived at SendSS staging was indicated in blue. During the period of this study, Georgia processed data into its SendSS system at 6:30am and 11:30am every day and sent data to the ESSENCE system at 1pm each day. These times are highlighted on the plot in green, and red respectively. New potential improved times, based on the kernel density of data being available in SendSS staging, are shown in the lighter shades of these colors at 8:30am and 12pm every day, while being sent to ESSENCE at 9am and 12:30pm to ensure time for data to be properly processed. These were determined to be optimal times for reducing lag in the data, however, may not be optimal for daily analysis. The purple line on the plot represents the times that data were available in ESSENCE’s system for analysis. This was notably delayed by a median 4.15 hours after the data was sent to ESSENCE on a typical day. Conclusions A data driven approach to choosing processing times could improve timeliness of data analyses in the SendSS and ESSENCE systems. By conducting this type of analysis in an ongoing periodic basis, processing lag times can be kept at a minimum. 1. Centers for Disease Control. Progress in improving state and local disease surveillance--United States, 2000-2005. MMWR Morbidity and mortality weekly report. 2005;54(33):822-825. 2. Jajosky R, Groseclose S. Evaluation of reporting timeliness of public health surveillance systems for infectious diseases. BMC Public Health. 2004;4(1). 3. Travers D, Barnett C, Ising A, Waller A. Timeliness of emergency department diagnoses for syndromic surveillance. AMIA Annual Symposium Proceedings. 2006;Vol. 2006:769. 4. Ward M, Brandsema P, van Straten E, Bosman A. Electronic reporting improves timeliness and completeness of infectious disease notification, The Netherlands, 2003. Eurosurveillance. 2005;10(1):7-8.

In response to the 2014 Ebola virus disease (EVD) outbreak in West Africa, the Georgia Department of Public Health developed the Infectious Disease Network (IDN) based on an EVD preparedness needs assessment of hospitals and Emergency Medical Services (EMS) providers. The network consists of 12 hospitals and 16 EMS providers with staff specially trained to provide a coordinated response and utilize appropriate personal protective equipment (PPE) for the transport or treatment of a suspected or confirmed serious communicable disease patient. To become a part of the network, each hospital and EMS provider had to demonstrate EVD capabilities in areas such as infection control, PPE, waste management, staffing and ongoing training, and patient transport and placement. To establish the network, the Georgia Department of Public Health provided training and equipment for EMS personnel, evaluated hospitals for EVD capabilities, structured communication flow, and defined responsibilities among partners. Since March 2015, the IDN has been used to transport, treat, and/or evaluate suspected or confirmed serious communicable disease cases while ensuring health care worker safety. Integrated infectious disease response systems among hospitals and EMS providers are critical to ensuring health care worker safety, and preventing or mitigating a serious communicable disease outbreak. (Disaster Med Public Health Preparedness. 2018;12:765-771)

We describe a botulism outbreak involving 4 Middle Eastern men complicated by delayed diagnosis, ambiguous epidemiologic links among patients, and illness onset dates inconsistent with a point-source exposure. Homemade turshi, a fermented vegetable dish, was the likely cause. Patients ate turshi at 2 locations on different days over 1 month.

Background: The annual Georgia Immunization Study (GIS) employs a retrospective cohort design to determine the up-to-date (UTD) immunization rate of 24-month-old children in Georgia. Previous results have shown lower vaccination rates in the second year of life, particularly for DTaP. We sought to determine if a discontinuation of Medicaid coverage after the infant year contributed to lower immunization rates in the second year. Methods: A stratified random sample of 2,002 Georgia children born in January 2013 was drawn from electronic birth records. Immunization history and Medicaid status were obtained from the Georgia Registry of Immunization Transactions and Services (GRITS). Parents and providers of children inadequately immunized according to the Advisory Committee on Immunization Practices’ (ACIP) immunization schedule were contacted for additional information. UTD immunization rates were compared among participants based on Medicaid status (Medicaid both years, first year only, second year only, never on Medicaid). The relationship between Medicaid status and specific immunizations was also explored. Reasons for loss of Medicaid among children who were not UTD were sought via parent interview. Significance testing was performed using Chi-Square tests in SAS version 9.4. Results: Children covered by Medicaid both years or never covered by Medicaid were more likely to be UTD by 24 months (90.4% and 84.5%, respectively) than children no longer covered by Medicaid in their second year of life (49.2%). These children also demonstrated a significantly lower immunization rate for the 4th DTaP dose (p

Objective To describe how flexible surveillance systems can be rapidly adapted and deployed, and increase the efficiency and accuracy of surveillance, during responses to outbreaks and all hazard emergent events. Introduction Georgia Department of Public Health (DPH) epidemiologists have responded to multiple emergent outbreaks with diverse surveillance needs. During the 2009 H1N1 influenza response, it was necessary to electronically integrate multiple reporting sources and view population-level data, while during the 2014–2015 West African Ebola epidemic, it was necessary to easily collect and view individual level data from travelers to facilitate early detection of potential imported Ebola disease. DPH in-house information technology (IT) staff work closely with epidemiologists to understand and accommodate surveillance needs. Through this collaboration, IT created a robust electronic surveillance and outbreak management system (OMS) to accommodate routine reporting of notifiable diseases and outbreak investigations, and surveillance during emergent events. Methods OMS was created within the State Electronic Notifiable Disease Surveillance System (SendSS); a secure, HIPAA-compliant, Oracle and web-based platform which collects data on all notifiable diseases in Georgia. This flexible platform has multi-functionality including dynamic web-based surveys that link to case records or outbreaks, online case reporting, electronic laboratory reporting, contact tracing, visual dashboards summarizing outbreak data, electronic alerts, and individual accounts for users with varying privileges to limit access to specific modules. These features can be customized for any emergent event. Results SendSS and OMS are widely used by state and district epidemiologists. Individual case and outbreak management activities include but are not limited to: notifiable disease and condition cases; all disease clusters; animal bites surveillance including bite investigation and laboratory results; and syndromic surveillance data automatically collected from 90 emergency facilities. OMS has been rapidly modified to facilitate efficient epidemiologic responses to emergent events such as: integrating multiple reporting sources during the H1N1 outbreak; shelter surveillance during hurricanes Katrina and Rita in 2005; active monitoring of >2,500 travelers in Georgia during the Ebola response; tracking cases investigations during the Zika response, and future monitoring of poultry workers if highly- pathogenic avian influenza occurs in Georgia. Conclusions The flexible and customizable features of SendSS and OMS accommodate the changing needs of epidemiologists to monitor a variety of diseases. Rapid implementation has enabled DPH epidemiologists to respond efficiently to emergent events using limited human resources, achieving immediate situational awareness by incorporating multiple data sources into user friendly dashboards and notifications, and easily sharing information among state and federal stakeholders to facilitate rapid risk assessment and response as needed. The success of these systems illustrates the return on DPH’s preparedness investment in retaining technical staff to work with epidemiologists to meet urgent surveillance needs.

Objective Describe how the Georgia Department of Public Health (DPH) uses syndromic surveillance to initiate review by District Epidemiologists (DEs) to events that may warrant a public health response ( 1 ). Introduction DPH uses its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) Module to collect, analyze and display results of emergency department patient chief complaint data from hospitals throughout Georgia. Methods DPH prepares a daily SS report, based upon the analysis of daily visits to 112 Emergency Department (EDs). The visits are classified in 33 syndromes. Queries of chief complaint and discharge diagnosis are done using the internal query capability of SendSS-SS and programming in SAS/BASE. Charting of the absolute counts or percentage of ED visits by syndromes is done using the internal charting capability of SendSS-SS. A daily SS report includes the following sections: Statewide Emergency Department Visits by Priority Syndromes ( Bioterrorism, BloodyRespiratory, FeverRespiratory, FeverChest, FeverFluAdmit, FeverFluDeaths, VeryIll , and PoxRashFever, Botulism, Poison, BloodyDiarrhea, BloodyVomit, FeverGI, ILI, FeverFlu, RashFever, Diarrhea, Vomit ). Statewide Flag Analysis : Is intended to detect statewide flags, by using the Charts capability in SendSS SS. Possible cases with presumptive diagnosis of potentially notifiable diseases : Is intended to provide early-warning to the DEs of possible cases that are reportable to public health immediately or within 7 days using queries in the Chief Complaint and Preliminary Diagnosis fields of SendSS-SS. Possible clusters of illness : Since any cluster of illness must be reported immediately to DPH, this analysis is aimed at querying and identifying possible clusters of patients with similar symptoms ( 2 ). Possible travel-related illness : Is intended to identify patients with symptoms and recent travel history. Other events of interest : Exposures to ill patients in institutional settings (e.g. chief complaint indicates that other children in the daycare have similar symptoms). Trend Analysis : Weekly analysis of seasonality and trends of 14 syndromes. Finally, specific events are notified to and reviewed by the 18 DEs, who follow up by contacting the Infection Preventionists of the hospitals to identify the patients using medical records or other hospital-specific identification numbers and follow up on the laboratory test results. Results Since 05/15/2016, 12 travel-related illnesses, 29 vaccine- preventable diseases, 14 clusters, and 3 chemical exposures have been notified to DEs. For instance, a cluster of chickenpox in children was identified after the DE contacted the Infection Preventionist of a hospital, who provided the DE with the laboratory results and the physician notes about the symptoms of the patients. These actions have resulted in earlier awareness of single cases or cluster of illness, prompt reporting of notifiable diseases, and successful interaction between DEs and health care providers. In addition, SS continues to track the onset, peak, and decline of seasonal illnesses. Conclusions The implementation of SS in the State of Georgia is helping with the timely detection and early responses to disease events and could prove useful in reducing the disease burden caused by a bioterrorist attack.

Background Mycobacterium abscessus is an uncommon cause of invasive odontogenic infection. Methods M abscessus-associated odontogenic infections occurred in a group of children after they each underwent a pulpotomy. A probable case-child was defined as a child with facial or neck swelling and biopsy-confirmed granulomatous inflammation after a pulpotomy between October 1, 2013, and September 30, 2015. M abscessus was isolated by culture in confirmed case-children. Clinical presentation, management, and outcomes were determined by medical record abstraction. Results Among 24 children, 14 (58%) were confirmed case-children. Their median age was 7.3 years (interquartile range, 5.8-8.2 years), and the median time from pulpotomy to symptom onset was 74 days (range, 14-262 days). Clinical diagnoses included cervical lymphadenitis (24 [100%] of 24), mandibular or maxillary osteomyelitis (11 [48%] of 23), and pulmonary nodules (7 [37%] of 19). Each child had ≥1 hospitalization and a median of 2 surgeries (range, 1-6). Of the 24 children, 12 (50%) had surgery alone and 11 (46%) received intravenous (IV) antibiotics. Nineteen of the 24 (79%) children experienced complications, including vascular access malfunction (7 [64%] of 11), high-frequency hearing loss (5 [56%] of 9), permanent tooth loss (11 [48%] of 23), facial nerve palsy (7 [29%] of 24), urticarial rash (3 [25%] of 12), elevated liver enzyme levels (1 [20%] of 5), acute kidney injury (2 [18%] of 11), incision dehiscence/fibrosis (3 [13%] of 24), and neutropenia (1 [9%] of 11). Conclusions M abscessus infection was associated with significant medical morbidity and treatment complications. Unique manifestations included extranodal mandibular or maxillary osteomyelitis and pulmonary nodules. Challenges in the identification of case-children resulted from an extended incubation period and various clinical manifestations. Clinicians should consider the association between M abscessus infection and pulpotomy in children who present with subacute cervical lymphadenitis. The use of treated/sterile water during pulpotomy might prevent further outbreaks.

Rural Georgians are more likely to be living in poverty, underinsured or uninsured, have a chronic condition such as heart disease or diabetes, and live in an area with a shortage of mental health, dental, and medical primary care providers than their urban counterparts (SORH, 2015). Rural people living with HIV (PLWH) face additional challenges of stigma, shortage of HIV care providers, and isolation. In 2014, PLWH in rural Georgia managed to access HIV care at the same or higher proportion than their urban counterparts, but among those retained in care, a lower proportion of rural PLWH achieved viral suppression. Potential factors include patient-, provider-, and societal-level obstacles. Meeting the needs of an increasing number of PLWH in the face of a diminishing HIV workforce is a daunting task in Georgia, and likely to be exacerbated in rural areas in coming years, unless there is a paradigm shift in our public health and medical team model for HIV prevention and care.

An outbreak at a university in Georgia was identified after 83 cases of probable pneumonia were reported among students. Respiratory specimens were obtained from 21 students for the outbreak investigation. The TaqMan array card (TAC), a quantitative PCR (qPCR)-based multipathogen detection technology, was used to initially identify Mycoplasma pneumoniae as the causative agent in this outbreak. TAC demonstrated 100% diagnostic specificity and sensitivity compared to those of the multiplex qPCR assay for this agent. All M. pneumoniae specimens ( n = 12) and isolates ( n = 10) were found through genetic analysis to be susceptible to macrolide antibiotics. The strain diversity of M. pneumoniae associated with this outbreak setting was identified using a variety of molecular typing procedures, resulting in two P1 genotypes (types 1 [60%] and 2 [40%]) and seven different multilocus variable-number tandem-repeat analysis (MLVA) profiles. Continued molecular typing of this organism, particularly during outbreaks, may enhance the current understanding of the epidemiology of M. pneumoniae and may ultimately lead to a more effective public health response.

Background Tools using local HIV data to help jurisdictions estimate future demand for medical and support services are needed. We present an interactive prevalence projection model using data obtainable from jurisdictional HIV surveillance and publically available data. Methods Using viral load data from Georgia’s enhanced HIV/AIDS Reporting System, state level death rates for people living with HIV and the general population, and published estimates for HIV transmission rates, we developed a model for projecting future HIV prevalence. Keeping death rates and HIV transmission rates for undiagnosed, in care/viral load >200, in care/viral load