Articles de revues sur le sujet « Fulton County Schools (Ga.) »

Abstract Background In 2019, Fulton County, GA was named one of 48 priority “hotspots” to target in renewed efforts to end the HIV epidemic in the United States. To more accurately predict women at greatest risk for HIV, we conducted an individually matched case–control study among women who attended a Fulton County health clinic to identify risk factors associated with HIV seroconversion. Methods We obtained data about women who sought care at Fulton County Board of Health Sexual Health Clinic (SHC) between 2011 and 2016. Cases were women with at least one clinician-assisted visit (CAV) at the SHC prior to HIV diagnosis date. Controls were women who visited the clinic in this same period but remained HIV negative. Controls were individually matched to cases in a 2:1 matching ratio on race, age at first CAV, and date of first CAV. Conditional logistic regression was used to develop a model for predicting probability of and identifying risk factors for HIV seroconversion. Results Of 18,281 women who were HIV negative at their first visit to the SHC between 2011 and 2016, 110 (0.6%) seroconverted before 2018. Of these, 80 (73%) had a CAV prior to HIV diagnosis. Using these 80 cases and 160 matched controls, having a history of gonorrhea, multiple gonorrhea episodes, a history of syphilis, a greater number of sex partners in the past 2 months, anal sex, history of injection drug or crack cocaine use, a history of exchanging drugs/money for sex, and heterosexual sex with more than one sex partner in the last month were associated with HIV seroconversion in bivariate analyses. After conducting backward selection from a fully adjusted model, predictors remaining were: having a history of syphilis (OR = 4.9, 95% CI: 1.4, 16.9), anal sex (OR = 2.9, 95% CI: 1.0, 8.3), and injection drug or crack cocaine use (OR = 34.8, 95% CI: 3.7, 328.1). Women having all three risk factors were six times more likely to seroconvert compared with matched controls without these risk factors. Conclusion Our results offer clinical insights into which women are most at-risk for HIV and are therefore best candidates for initiating HIV prevention interventions like pre-exposure prophylaxis (PrEP) within a HIV “hotspot” in the South. Disclosures All Authors: No reported Disclosures.

This is an interview with Alice Ansley, Literacy Coordinator at Dalton County Schools (GA). Dalton County is an ethnically and language-diverse school district with the majority of students receiving free or reduced lunch. Alice Ensley has been recognized for her leadership in literacy education, particularly in regard to facing the challenges of the COVID-19 virus.

Abstract Food security amongst community-dwelling older adults attending senior centers is not often examined. It is an integral determinant of health because of the impact on multiple health conditions, which are further impacted by functional impairments and co-morbidities that can come with increasing age. Additionally, meal and nutrition education providers do not typically collect this information, which can be used to better provide services to their clients. This study examined the relationship between food security and demographic variables of older adults attending senior centers in Georgia served by nutrition provider Open Hand Atlanta (OHA). One hundred sixty-five senior center attendees were recruited from nine senior centers throughout Fulton County. The age of the participants ranged between 52 to 97, with most identifying as female (86.7%) and African American (79%). Food security (outcome of interest) was measured by the USDA 6-item module. Bivariate associations were tested between food security and ADLs and iADLs (instrumental ADLs) measured by a 15-item list, health conditions, shopping habits, transportation, caregiving, and demographics. Results showed that food security differed based on race/ethnicity X2 (n=165) = 8.94 (df=2), p = .011 and type of insurance X2 (n=100) = 5.95, (df=1) p = 0.015). In addition, those who are under public/Govt. insurance experience a higher proportion of food insecurity compared to those under private insurance (p=.015). Results have implications for nutrition providers when offering additional services to senior centers and the older adults they serve.

Background: Due to reliance on hospital discharge data for case identification, the burden of noninvasive and community-acquired S. aureus disease is often underestimated. To determine the full burden of S. aureus infections, we utilized population-based surveillance in a large urban county. Methods: The Georgia Emerging Infections Program (GA EIP) conducted CDC-funded, population-based surveillance by finding cases of S. aureus infections in 8 counties around Atlanta in 2017. Cases were residents with S. aureus isolated from either a normally sterile site in a 30-day period (invasive cases) or another site in a 14-day period (noninvasive cases). Medical records (all invasive and 1:4 sample of noninvasive cases) among Fulton County residents were abstracted for clinical, treatment, and outcome data. Cases treated were mapped to standard therapeutic site codes. Noninvasive specimens were reviewed and attributed to an invasive case if both occurred within 2 weeks. Incidence rates were calculated using 2017 census population and using a weight-adjusted cohort to account for sampling. Results: In total, 1,186 noninvasive (1:4 sample) and 529 invasive cases of S. aureus in Fulton county were reviewed. Only 35 of 1,186 (2.9%) noninvasive cases were temporally linked to invasive cases, resulting in 5,133 cases after extrapolation (529 invasive, 4,604 noninvasive). All invasive cases and 3,776 of 4,604 noninvasive cases (82%) were treated (4,305 total). Treatment was highest in skin (90%) and abscess (97%), lowest in urine (62%) and sputum (60%), and consisted of antibacterial agents alone (65%) or in addition to drainage procedures (35%). Overall, 41% of all cases were hospitalized, 12% required ICU admission, and 2.7% died, almost exclusively with bloodstream and pulmonary infections. Attribution of noninvasive infection was most often outside healthcare settings (87%); only 341 (7.9%) were hospital-onset cases; however, 34% of cases had had healthcare exposure in the preceding year, most often inpatient hospitalization (75%) or recent surgery (35%). Estimated countywide incidence was 414 per 100,000 (130 for MRSA and 284 for MSSA), invasive infection was 50 per 100,000. Among treated cases, 57% were SSTI, and the proportion of cases caused by MRSA was ~33% but varied slightly by therapeutic site (Fig. 1). Conclusions: The incidence of treated S. aureus infection in our large urban county is estimated to be 414 per 100,000 persons, which exceeds previously estimated rates based on hospital discharge data. Only 12% of treated infections were invasive, and <1 in 10 were hospital onset. Also, two-thirds of treated disease cases were MSSA; most were SSTIs.Funding: Proprietary Organization: Pfizer.Disclosures: Scott Fridkin, consulting fee - vaccine industry (spouse).

Abstract Background Tuberculosis (TB) causes significant morbidity and mortality in US cities, particularly in poor, transient populations. During a TB outbreak in Fulton County, Atlanta, GA, we aimed to determine whether local maps created from multiple locations of personal activity per case would differ significantly from traditional maps created from single residential address. Methods Data were abstracted for patients with TB disease diagnosed in 2008–2014 and receiving care at the Fulton County Health Department. Clinical and activity location data were abstracted from charts. Kernel density methods, activity space analysis, and overlay with homeless shelter locations were used to characterize case spatial distribution when using single versus multiple addresses. Results Data were collected for 198 TB cases, with over 30% homeless US-born cases included. Greater spatial dispersion of cases was found when utilizing multiple versus single addresses per case. Activity spaces of homeless and isoniazid (INH)-resistant cases were more spatially congruent with one another than non-homeless and INH-susceptible cases (P &lt; .0001 and P &lt; .0001, respectively). Conclusions Innovative spatial methods allowed us to more comprehensively capture the geography of TB-infected homeless persons, who made up a large portion of the Fulton County outbreak. We demonstrate how activity space analysis, prominent in exposure science and chronic disease, supports that routine capture of multiple location TB data may facilitate spatially different public health interventions than traditional surveillance maps.

Abstract Background Contact tracing during the Coronavirus Disease 2019 (COVID-19) pandemic in the USA has been met with various challenges. In an attempt to improve the yield of close contact collection, the Fulton County Board of Health implemented a pilot approach to contact elicitation at the time of testing. Methods Between October and November 2020, close contacts were elicited from persons under investigation (PUIs) at one COVID-19 testing site in Fulton County, GA. Secure online data collection forms were used to record PUI demographic data, close contact information and reasons for not providing contacts. Results Of 1238 PUIs, 48% reported at least one contact. Among the 66 people who tested positive, 16 (24%) reported contacts compared to 578/1165 (50%) who tested negative. PUIs of increasing age were less likely to provide contacts; Black and Hispanic PUIs were also less likely to report any contacts compared to White and Asian PUIs. Conclusions Our study revealed that PUIs testing positive were less likely to provide contacts compared to PUIs testing negative. Age and racial differences were also noted in the provision of contacts. Further investigation is needed to understand these discrepancies in order to devise more effective strategies for contact elicitation.

Abstract Disseminated gonococcal infection (DGI) is a rare complication caused by the systemic dissemination of Neisseria gonorrhoeae (Ng) to normally sterile anatomical sites. Little is known about the genetic diversity of DGI gonococcal strains and how they relate to other gonococcal strains causing uncomplicated mucosal infections. We used whole genome sequencing to characterize DGI isolates (n = 30) collected from a surveillance system in Georgia (GA), USA during 2017-2020 to understand phylogenetic clustering among DGI as well as uncomplicated uro-and-extragenital gonococcal (UGI) isolates (n = 110) collected in Fulton County, GA during 2017-2019. We also investigated the presence or absence of genetic markers related to antimicrobial resistance (AMR) as well as surveyed the genomes for putative virulence genetic factors associated with normal human-serum (NHS) resistance that might facilitate DGI. We found that DGI strains demonstrated significant genetic variability similar to the population structure of isolates causing UGI, with sporadic incidences of geographically clustered DGI strains. DGI isolates contained various AMR markers and genetic mechanisms associated with NHS resistance. DGI isolates had a higher frequency of the porB1A allele compared with UGI (67% vs. 9%, p &lt; 0.0001); however, no single NHS resistance marker was found in all DGI isolates. Continued DGI surveillance with genome-based characterization of DGI isolates is necessary to better understand specific factors that promote systemic dissemination.

Abstract Background Incidence estimates of Staphylococcus aureus infections rarely include the full spectrum of clinically relevant disease from both community and healthcare settings. Methods We conducted a prospective study capturing all S. aureus infections in Fulton County, Georgia, during 2017. Medical records of patients with any incident infection (clinical cultures growing S. aureus from any site, without prior positive culture in previous 14 days) were reviewed. Estimates of disease incidence were calculated using age, race, and sex specific population denominators accounting for weighted sampling methods. Multivariable logistic regression models were used to identify risk factors for hospitalization among patients with skin/soft tissue infections (SSTIs). Results The overall incidence of clinically relevant S. aureus infection was 405.7 cases per 100,000 people (SE 5.62, range 400.1 - 411.3). Overall incidence for those of Black race was 500.84 cases per 100,000 people (SE 14.55), while White race patients had overall incidence of 363.67 cases per 100,000 people (SE 13.8). SSTIs were most common infection (2,351; 225.8 cases per 100,000 people, SE 7.1), and 30% required hospitalization. Among SSTI, after adjusting for invasive disease, cellulitis, diabetes, and demographics, independent predictors of hospitalization included MRSA (aOR 1.6; 95% CI 1.0 - 2.7), and homelessness (aOR 4.9; 95% CI 1.1-22) Conclusions The burden of clinically relevant S. aureus infections is high, particularly among the Black population, and risks for hospitalization among SSTI include isolate factors and factors related to patients’ vulnerability.

Abstract Background Machine Learning (ML) algorithms have predicted incident HIV using electronic medical record (EMR) data. We developed an ML model using de-identified public health data from a high-incidence area to predict incident HIV which could inform public health interventions such as HIV testing, education, and scale-up prevention strategies. Methods We used de-identified public health data from Georgia’s State Electronic Notifiable Disease Surveillance System (SendSS) and Enhanced HIV/AIDS Reporting System (eHARs) from 01/2010 to 12/2021 in Fulton County - GA. Included variables are displayed in Table 1. We included males, 13 years of age and older. Patient's HIV status and HIV incidence during the study period were confirmed by matching individuals between the datasets. We excluded individuals diagnosed with HIV before 2010, those with an HIV diagnosis as their first sexually transmitted infection (STI) dataset entry, and those individuals with more than 10% of variables missing. We matched a social vulnerability index (SVI) to an individual census tract. We trained various ML classification models with an equal number of HIV-positive and randomly selected HIV-negative observations to balance both the training (85%) and test sets (15%) to predict incident HIV.Table 1.Data sources and variables included in the model From datasets in Fulton County - Georgia 2010-2021. ID=Identification SVI=Social vulnerability index STI= Sexually transmitted infection HIV=human immunodeficiency virus CAT=categorical CONT=continuos Results Of 85,224 individuals, a total of 1,698 male individuals (2%) were confirmed positive for HIV during the study period and met our inclusion criteria. The training set included 2,896 observations (1,448 HIV+ and 1,448 HIV-) and the test set included 500 observations (250 HIV+ and 250 HIV-). Among the ML models used, Gradient Boosted Trees and Random Forest achieved an accuracy as high as 80% for correctly predicting incident HIV in the test set. The most predictive features were mean age at STI diagnosis, STI diagnosing provider type, STI diagnosis interval, SVI Theme 1, and STI diagnosis. Model performance and evaluation are presented in Figure 1.Figure 1.Machine Learning Classifier Performance Evaluation on Test Set. Random Forest (RF) and Gradient Boosted Trees (GBT) confusion matrix on a test set of 500 observations. Overall both RF and GBT models achieved an overall high accuracy in correctly predicting incident HIV in 202/250 (79%) and 204/250 (80%) individuals respectively. Precision= number of true positives divided by the total number of positive predictions; Recall= Percentage of observation the model correctly identifies as belonging to their class; F-1 Score= Combined score of precision and recall. Conclusion Our ML models can accurately predict incident HIV and can be used to customize outreach activities. The approach used is unique in that it strictly relies on de-identified STI reporting public health data, which makes it suitable for a broader population than EMR data. However, more research is needed to implement and evaluate these models in actual public health interventions. Disclosures All Authors: No reported disclosures

ObjectiveTo utilize ED chief complaint data obtained from syndromic surveillance to quantify the effect of the Illinois smoking ban on acute myocardial infarction (AMI), acute coronary syndrome (ACS), stroke, and chronic obstructive pulmonary disease (COPD) related ED visits in adults in Cook County, IL.IntroductionTobacco use is the leading global cause of preventable death, killing more than five million people per year [1]. In addition, exposure to secondhand smoke is estimated to kill an additional 600,000 people globally each year [1]. In 1986, the US Surgeon General’s Report declared secondhand smoke to be a cause of lung cancer in healthy nonsmokers [2].The first law restricting smoking in public places was enacted in 1973 in Arizona that followed the 1972 Surgeon General’s Report providing awareness of the negative health effects associated with the exposure to air pollution from tobacco smoke [3]. Smoke-free laws were slowly enacted after this time point with most occurring after the year 2000 [4].In July 2007, the Smoke Free Illinois Act (SB0500, Public Act 095-0017) was passed in IL [5]. The ban went into effect on Jan 1, 2008 and Illinois joined 22 other states in prohibiting smoking in virtually all public places and workplaces including offices, theaters, museums, libraries, schools, commercial establishments, retail stores, bars, private clubs, and gaming facilities [5-6].While many studies have examined the effect of smoking bans on hospitalizations, this study would be the first to examine the effect of the comprehensive smoking ban in IL on ED visits by utilizing chronic disease categories created with ED chief complaint data captured by syndromic surveillance [7]. The author hypothesizes that the comprehensive smoking ban in IL significantly reduced the ED visits associated with AMI, ACS, stroke, and COPD in adults in Cook County, IL.MethodsED visits with chief complaints consistent with categories for AMI, ACS, stroke and COPD captured by the Cook Co. Dept. of Public Health local instance of ESSENCE from Jan 1, 2006 – Dec 31, 2013 were included in the analysis. Proc Genmod with a log link and negative binomial distribution was utilized for the analysis. All data was aggregated at the monthly level. The total number of ED visits of the health effect of interest was the dependent variable. The total ED visits during the same period of time, was used as the offset variable, sub-grouped by age and gender where appropriate. A binary variable was utilized to capture the effect of the time period after the implementation of the statewide smoking ban; 0 for before the ban and 1 for after the ban. When examining the effect of the statewide ban, Cook Co. as an entirety was examined as well as ED visits stratified by zip codes that already had a smoking ban in place at that time point and those that did not, and stratifying by urban (Chicago) vs. suburban Cook Co. Seasonality was addressed by including month, month squared and month cubed in the model. Influenza was addressed by including a binary variable to indicate when influenza was occurring in the area based on percent influenza-like-illness ED visits that were occurring above the threshold for the area during that time period. Age and gender were also evaluated as confounders and effect modifiers. SAS 9.4 was utilized to perform the analyses.ResultsResults are presented in Table 1. Reductions of ED visits after the smoking ban implementation were seen in AMI and ACS disease categories for the overall adjusted model, at 3% and 3.5% respectively. Stroke associated ED visits were not affected by the smoking ban. COPD associated ED visits were not reduced immediately by the smoking ban, but did have a significant reduction 6 months after implementation of the ban at 3.6%. Stronger effects were seen in individuals 70 years and older, females, the urban population, and zip codes without a prior ban for AMI, ACS, and COPD.ConclusionsAn immediate, significant reduction in ED visits associated with AMI and ACS was associated with the IL statewide smoking ban in Cook Co., IL. COPD associated ED visits were significantly reduced 6 months after the ban implementation. The effect was greater in individuals 70 years and older, females, the urban population, and zip codes without a prior ban.References1. WHO, WHO report on the global tobacco epidemic. Implementing smoke-free environments. 2009, WHO: Geneva, Switzerland.2. DC, The health consequences of involuntary exposure to tobacco smoke : a report of the Surgeon General. 2006, U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, Coordinating Center for Health Promotion, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health: Atlanta, GA.3. Eriksen, M. and F. Chaloupka, The economic impact of clean indoor air laws. CA Cancer J Clin, 2007. 57(6): p. 367-78.4. Foundation, A.N.R. Overview List - How many Smokefree Laws? 2015 10/2/2015 [cited 2015 10/5/2015]; Available from: http://www.no-smoke.org/pdf/mediaordlist.pdf.5. Smoke Free Illinois Act, in Public Act 095-0017. 2007.6. Goodman, P., et al., Effects of the Irish smoking ban on respiratory health of bar workers and air quality in Dublin pubs. Am J Respir Crit Care Med, 2007. 175(8): p. 840-5.7. Callinan, J.E., et al., Legislative smoking bans for reducing secondhand smoke exposure, smoking prevalence and tobacco consumption. Cochrane Database Syst Rev, 2010(4): p. CD005992.