scholarly journals 187. Characterizing Household Clustering of COVID-19 Cases in Fulton County, Georgia, June 2020–April 2021

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S113-S114
Author(s):  
Carol Liu ◽  
Sasha Smith ◽  
Allison Chamberlain ◽  
Neel Gandhi ◽  
Fazle N Khan ◽  
...  
Keyword(s):  
Census Data ◽  
Positive Sample ◽  
Contact Tracing ◽  
Point Estimate ◽  
Sample Collection ◽  
Close Proximity ◽  
State Surveillance ◽  
Race Ethnicity ◽  
Household Cluster ◽  
Over Time

Abstract Background Households are important for SARS-CoV-2 transmission due to close proximity in enclosed living spaces over long durations. Using contact tracing, the secondary attack rate in households is estimated at 18-20%, yet no studies have examined COVID-19 clustering within households, an important measure to inform testing and prevention. We sought to quantify and characterize household clustering of COVID-19 cases in Fulton County, Georgia. Methods We used state surveillance data to identify all PCR- or antigen-confirmed cases of COVID-19 in Fulton County. Clustered cases were defined as cases with matching street address, including unit number. Communal places (e.g., nursing homes, correctional facilities) were excluded, as were apartments missing unit number. Household clusters were defined as ≥2 COVID-19 cases at the same residential address with positive sample collection dates within 14 days of one another. We described proportion of COVID-19 cases that were clustered, stratified by age, sex, and race/ethnicity over time. Results There were 60,614 COVID-19 cases with available address reported in Fulton County during 6/1/20–4/30/21. Of these, 25,149 (41.6%) had an address that matched at least one other case; 20,793 (34.3%) were from 8,582 household clusters with positive sample collection dates within 14 days (Fig 1). Majority of clusters had 2 individuals (N=6119, 71%), though some had ≥6 individuals (N=79, 0.9%). Clustering increased through January 2021 (Fig 2). Children were more likely to be in household clusters (Fig 4) and 15% of clusters had a child as first diagnosed case with increases since January 2021 (Fig 3). Consistently higher clustering was observed among Hispanic persons, with rising clustering among Asian persons (Fig 5). Figure 1. Distribution of household-clustered COVID-19 cases in Fulton county between June, 2020 and April 2021 Figure 2. Proportion of COVID-19 cases that were part of a household cluster, Fulton County, June 2020–April 2021. Error bars denote 95% confidence interval around the point estimate. Figure 3. Age of first diagnosis among households with at least 2 cases diagnosed within 14 days Conclusion One-third of COVID-19 cases in Fulton County were part of a household cluster. The higher proportion of children in household clusters likely reflects higher probability of living in a home with an adult caregiver. Higher household clustering among Hispanic and Asian persons, regardless of age, may reflect larger households (supported by census data) or increased exposures outside the house. Timely testing for household members to prevent ongoing transmission remains essential. Figure 4. Proportion of COVID-19 cases that were part of a household cluster, by age – Fulton County, June 2020–March 2021 Proportion of COVID-19 cases that were part of a household cluster in Fulton County stratified by race/ethnicity over time Disclosures All Authors: No reported disclosures

scholarly journals COVID-19 Contact Tracing Outcomes in Washington State, August and October 2020

2021 ◽  
Vol 9 ◽  
Author(s):  
Robert A. Bonacci ◽  
Lillian M. Manahan ◽  
James S. Miller ◽  
Patrick K. Moonan ◽  
Missy B. Lipparelli ◽  
...  
Keyword(s):  
Symptom Onset ◽  
Demographic Characteristics ◽  
Washington State ◽  
Contact Tracing ◽  
Time Interval ◽  
Case Investigation ◽  
Specimen Collection ◽  
Race Ethnicity ◽  
Test Result ◽  
Over Time

Introduction: Case investigation and contact tracing are important tools to limit the spread of SARS-CoV-2, particularly when implemented efficiently. Our objective was to evaluate participation in and timeliness of COVID-19 contact tracing and whether these measures changed over time.Methods: We retrospectively assessed COVID-19 case investigation and contact tracing surveillance data from the Washington State centralized program for August 1–31, 2020 and October 1–31, 2020. We combined SARS-CoV-2 testing reports with contact tracing data to compare completeness, reporting of contacts, and program timeliness.Results: For August and October respectively, 4,600 (of 12,521) and 2,166 (of 16,269) individuals with COVID-19 were referred to the state program for case investigation. Investigators called 100% of referred individuals; 65% (August) and 76% (October) were interviewed. Of individuals interviewed, 33% reported contacts in August and 45% in October, with only mild variation by age, sex, race/ethnicity, and urbanicity. In August, 992 individuals with COVID-19 reported a total of 2,584 contacts (mean, 2.6), and in October, 739 individuals reported 2,218 contacts (mean, 3.0). Among contacts, 86% and 78% participated in interviews for August and October. The median time elapsed from specimen collection to contact interview was 4 days in August and 3 days in October, and from symptom onset to contact interview was 7 days in August and 6 days in October.Conclusions: While contact tracing improved with time, the proportion of individuals disclosing contacts remained below 50% and differed minimally by demographic characteristics. The longest time interval occurred between symptom onset and test result notification. Improving elicitation of contacts and timeliness of contact tracing may further decrease SARS-CoV-2 transmission.

scholarly journals Predictors of historical change in drug treatment coverage among people who inject drugs in 90 large metropolitan areas in the USA, 1993–2007

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Barbara Tempalski ◽  
Leslie D. Williams ◽  
Brooke S. West ◽  
Hannah L. F. Cooper ◽  
Stephanie Beane ◽  
...  
Keyword(s):  
Public Health ◽  
Drug Treatment ◽  
Resource Availability ◽  
People Who Inject Drugs ◽  
Census Data ◽  
Poverty Rate ◽  
Community Action ◽  
Historical Change ◽  
Treatment Coverage ◽  
Over Time

Abstract Background Adequate access to effective treatment and medication assisted therapies for opioid dependence has led to improved antiretroviral therapy adherence and decreases in morbidity among people who inject drugs (PWID), and can also address a broad range of social and public health problems. However, even with the success of syringe service programs and opioid substitution programs in European countries (and others) the US remains historically low in terms of coverage and access with regard to these programs. This manuscript investigates predictors of historical change in drug treatment coverage for PWID in 90 US metropolitan statistical areas (MSAs) during 1993–2007, a period in which, overall coverage did not change. Methods Drug treatment coverage was measured as the number of PWID in drug treatment, as calculated by treatment entry and census data, divided by numbers of PWID in each MSA. Variables suggested by the Theory of Community Action (i.e., need, resource availability, institutional opposition, organized support, and service symbiosis) were analyzed using mixed-effects multivariate models within dependent variables lagged in time to study predictors of later change in coverage. Results Mean coverage was low in 1993 (6.7%; SD 3.7), and did not increase by 2007 (6.4%; SD 4.5). Multivariate results indicate that increases in baseline unemployment rate (β = 0.312; pseudo-p < 0.0002) predict significantly higher treatment coverage; baseline poverty rate (β = − 0.486; pseudo-p < 0.0001), and baseline size of public health and social work workforce (β = 0.425; pseudo-p < 0.0001) were predictors of later mean coverage levels, and baseline HIV prevalence among PWID predicted variation in treatment coverage trajectories over time (baseline HIV * Time: β = 0.039; pseudo-p < 0.001). Finally, increases in black/white poverty disparity from baseline predicted significantly higher treatment coverage in MSAs (β = 1.269; pseudo-p < 0.0001). Conclusions While harm reduction programs have historically been contested and difficult to implement in many US communities, and despite efforts to increase treatment coverage for PWID, coverage has not increased. Contrary to our hypothesis, epidemiologic need, seems not to be associated with change in treatment coverage over time. Resource availability and institutional opposition are important predictors of change over time in coverage. These findings suggest that new ways have to be found to increase drug treatment coverage in spite of economic changes and belt-tightening policy changes that will make this difficult.

scholarly journals 1136. Trends in Speaker Representation at the Infectious Diseases Society of America (IDSA) IDWeek Conference, 2013-2019

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S597-S597
Author(s):  
Jasmine R Marcelin ◽  
Rohan Khazanchi ◽  
Elizabeth Lyden ◽  
Kelly Cawcutt ◽  
Ravina Kullar ◽  
...  
Keyword(s):  
Gender Equity ◽  
Linear Increase ◽  
Program Committee ◽  
Chi Square ◽  
Poster Sessions ◽  
Professional Degree ◽  
Race Ethnicity ◽  
Chi Square Analysis ◽  
Changes Over Time ◽  
Over Time

Abstract Background Over the last decade, there have been sustained efforts to diversify the healthcare workforce. In 2016, the IDWeek Program Committee was charged to ensure gender equity in speaker sessions. Whether this intervention also resulted in more opportunities for underrepresented speakers has not been determined. Methods This project was supported by IDSA, who provided demographic information on IDWeek speakers (excluding poster sessions) from 2013-2019. Data were summarized using descriptive statistics, and chi-square analysis evaluated changes over time. Each speaker slot was considered an independent event. Data was combined for 2013-2016 (≤2016) and 2017-2019 (&gt;2016). IDSA membership demographics were available from 2014 for gender, race/ethnicity, from 2016 for age, and from 2018 for professional degree. Results A total of 3640 speaker slots were filled by 2504 individuals from 2013-2019. A larger proportion of speaker slots were filled by women &gt;2016 (51%) vs ≤ 2016 (43%), with a linear increase from 38.6% in 2013 to 52.1% in 2019 (p&lt; 0.001). Averaged across 2013-2019, IDSA membership was 67.5% White, 20.6% Asian, 7.7% Latinx, 3.9% Black, and 0.4% Other. IDWeek Speakers during that timeframe were 77.7% White, 13.9% Asian, 4.7% Latinx, 2.7% Black, and 1.0% Other; a larger proportion of slots were filled by Asian speakers &gt;2016 (16.3%) vs ≤ 2016 (12.8%) (p=0.005). The proportion of pharmacist speakers increased over time; 5.1% of speakers in 2019 reflected IDSA pharmacist membership (5.4%). The proportion of individuals invited to speak more than once differed by age (19% in &lt; 40yo, 28% 40-49yo, 32% 50-59yo, and 22% &gt;60yo; p&lt; 0.001), and professional degree (28% physicians, 18% pharmacists, 9% other doctorates, and 7% non-doctorate speakers; p&lt; 0.001). Figure 1: Trends in Gender Distribution of IDWeek Speakers and IDSA Members, 2013-2019 Figure 2: Trends in Race/Ethnicity Distribution of IDWeek Speakers and IDSA Members, 2013-2019 Conclusion Intentional consideration of gender equity by the Program Committee significantly improved equitable gender representation of invited speakers at IDWeek. This effort has not resulted in increased diversity of invited speakers from groups underrepresented in IDSA membership. To ensure that invited speakers represent the membership of IDSA/IDWeek partner organizations and more importantly, the communities we serve, we call for continued application of the principles of Inclusion, Diversity, Access, and Equity at IDWeek. Disclosures All Authors: No reported disclosures

scholarly journals Investigation of Four Clusters of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Rwanda, 2020

2021 ◽  
Vol 18 (13) ◽  
pp. 7018
Author(s):  
Olivier Nsekuye ◽  
Edson Rwagasore ◽  
Marie Aime Muhimpundu ◽  
Ziad El-Khatib ◽  
Daniel Ntabanganyimana ◽  
...  
Keyword(s):  
High Risk ◽  
Severe Acute Respiratory Syndrome ◽  
Symptom Onset ◽  
Attack Rate ◽  
Positive Sample ◽  
Contact Tracing ◽  
Face To Face ◽  
Laboratory Confirmation ◽  
Closed Environment ◽  
Index Cases

We reported the findings of the first Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) four clusters identified in Rwanda. Case-investigations included contact elicitation, testing, and isolation/quarantine of confirmed cases. Socio-demographic and clinical data on cases and contacts were collected. A confirmed case was a person with laboratory confirmation of SARS-CoV-2 infection (PCR) while a contact was any person who had contact with a SARS-CoV-2 confirmed case within 72 h prior, to 14 days after symptom onset; or 14 days before collection of the laboratory-positive sample for asymptomatic cases. High risk contacts were those who had come into unprotected face-to-face contact or had been in a closed environment with a SARS-CoV-2 case for >15 min. Forty cases were reported from four clusters by 22 April 2020, accounting for 61% of locally transmitted cases within six weeks. Clusters A, B, C and D were associated with two nightclubs, one house party, and different families or households living in the same compound (multi-family dwelling). Thirty-six of the 1035 contacts tested were positive (secondary attack rate: 3.5%). Positivity rates were highest among the high-risk contacts compared to low-risk contacts (10% vs. 2.2%). Index cases in three of the clusters were imported through international travelling. Fifteen of the 40 cases (38%) were asymptomatic while 13/25 (52%) and 8/25 (32%) of symptomatic cases had a cough and fever respectively. Gatherings in closed spaces were the main early drivers of transmission. Systematic case-investigations contact tracing and testing likely contributed to the early containment of SARS-CoV-2 in Rwanda.

scholarly journals Maintenance requirement and deposition efficiency of lysine in pigs

2013 ◽  
Vol 48 (9) ◽  
pp. 1269-1274 ◽  
Author(s):  
Marcos Speroni Ceron ◽  
Vladimir de Oliveira ◽  
Paulo Alberto Lovatto ◽  
Marcos Martinez do Vale
Keyword(s):  
Dry Matter ◽  
Deposition Efficiency ◽  
Growing Pigs ◽  
Dry Matter Intake ◽  
Sample Collection ◽  
Maintenance Requirement ◽  
Nitrogen Intake ◽  
Average Body ◽  
Over Time ◽  
Average Body Weight

The objective of this work was to determine the maintenance requirement and the deposition efficiency of lysine in growing pigs. It was used the incomplete changeover experimental design, with replicates over time. Twelve castrated pigs with average body weight (BW) of 52±2 kg were kept in metabolism crates with a controlled temperature of 22ºC. The diets were formulated to supply 30, 50, 60, and 70% of the expected requirements of standardized lysine, and provided at 2.6 times the energy requirements for maintenance. The trial lasted 24 days and was divided into two periods of 12 days: seven days for animal adaptation to the diet and five days for sample collection. The increasing content of lysine in the diet did not affect dry matter intake of the pigs. The amount of nitrogen excreted was 47% of the nitrogen intake, of which 35% was excreted through feces and 65% through urine. The estimated endogenous losses of lysine were 36.4 mg kg-1 BW0.75. The maintenance requirement of lysine for pigs weighing around 50 kg is 40.4 mg kg-1 BW0.75, and the deposition efficiency of lysine is 90%.

scholarly journals TraceTogether: Pandemic Response, Democracy, and Technology

2020 ◽  
Vol 14 (3) ◽  
pp. 523-532 ◽  
Author(s):  
Hallam Stevens ◽  
Monamie Bhadra Haines
Keyword(s):  
Citizen Science ◽  
Social Trust ◽  
Public Support ◽  
Contact Tracing ◽  
Democratic Participation ◽  
Government Power ◽  
Power And Control ◽  
Close Proximity ◽  
Pandemic Response ◽  
And Control

Abstract On 20 March 2020, in the midst of the COVID-19 pandemic, the Singapore government released a new app called TraceTogether. Developed by the Ministry of Health, SG United, and GovTech Singapore, the app uses the Bluetooth capability of smartphones to store information about other smartphones that have come into close proximity with your own. These data facilitate the government’s process of “contact tracing” through which they track those who have potentially come into contact with the virus and place them in quarantine. This essay attempts to understand what kinds of citizens and civic behavior might be brought into being by this technology. By examining the workings and affordances of the TraceTogether app in detail, the authors argue that its peer-to-peer and open-source technology features mobilize the rhetorics and ideals of citizens science and democratic participation. However, by deploying these within a context that centralizes data, the app turns ideals born of dissent and protest on their head, using them to build trust not within a community but rather in government power and control. Rather than building social trust, TraceTogether becomes a technological substitute for it. The significant public support for TraceTogether shows both the possibilities and limitations of citizen science in less liberal political contexts and circumstances.

scholarly journals Inequities in CLABSI Rates in a Children’s Hospital by Race, Ethnicity, and Language Preference

2021 ◽  
Vol 1 (S1) ◽  
pp. s43-s44
Author(s):  
Caitlin McGrath ◽  
Matthew Kronman ◽  
Danielle Zerr ◽  
Brendan Bettinger ◽  
Tumaini Coker ◽  
...  
Keyword(s):  
Reference Group ◽  
Tertiary Care ◽  
Central Line ◽  
Mucosal Barrier ◽  
Point Estimate ◽  
Language Preference ◽  
Children's Hospital ◽  
Children’S Hospital ◽  
Race Ethnicity ◽  
Clabsi Rate

Background: Systemic racism results in health inequities based on patient race, ethnicity, and language preference. Whether these inequities exist in pediatric central-line–associated bloodstream infections (CLABSIs) is unknown. Methods: This retrospective cohort study included patients with central lines hospitalized from October 2012 to June 2019 at our tertiary-care children’s hospital. Self-reported race, ethnicity, language preference, demographic, and clinical factors were extracted from the electronic health record. The primary outcome was non–mucosal barrier injury (non-MBI) CLABSI episodes as defined by the NHSN. CLABSI rates between groups were compared using χ2 tests and Cox proportional hazard regression. We adjusted for care unit, age, immunosuppressed status, diapered status, central-line type, line insertion within 7 days, daily CLABSI maintenance bundle compliance, number of blood draws and IV medication doses, and need for total parental nutrition, extracorporeal membrane oxygenation, and renal replacement therapy. In mid-2019, we engaged stakeholders in each care unit to describe preliminary findings and to identify and address potential drivers of observed inequities. Results: We included 337 non-MBI CLABSI events over 230,699 central-line days (CLDs). The overall non-MBI CLABSI rate during the study period was 1.46 per 1,000 CLDs. Unadjusted CLABSI rates for black or African American (henceforth, “black”), Hispanic, non-Hispanic white, and Asian (the 4 largest race or ethnicity groups by CLDs) patients were 2.74, 1.53, 1.42, 1.24 per 1,000 CLDs, respectively (P < .001) (Table 1). Unadjusted CLABSI rates for patients with limited-English proficiency (LEP) and English-language preference were 1.98 and 1.38 per 1,000 CLDs, respectively (P = .014). After adjusting for covariates, the hazard ratio (HR) point estimate for CLABSI rate remained higher for black patients (HR, 1.50; 95% CI, 0.99–2.28) and patients with LEP (HR, 1.33; 95% CI, 0.87–2.05), compared to the reference group based on largest CLD. The differences in CLABSI rate by race or ethnicity and language were more pronounced in 2 of our 6 care units. Stakeholder engagement and analysis of hospital data revealed opportunities on those units for improved (1) interpreter utilization and (2) line maintenance observation practices by race/ethnicity and language preference (data not shown). These findings and CLABSI rates over time by race/ethnicity and language preference (Figures 1 and 2) were shared with frontline staff. Conclusions: In our children’s hospital, CLABSI rates differed based on patients’ self-reported race, ethnicity, and language preference, despite controlling for factors commonly associated with CLABSI. Identifying inequities in CLABSI rates and mitigating their determinants are both essential to the goal of achieving equitable care.Funding: NoDisclosures: None

Abstract P299: Changes in Race-Sex Specific Population Attributable Fraction for Obesity-Related Diabetes, 2001-2016

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Natalie Cameron ◽  
Megan McCabe ◽  
Lucia Petito ◽  
Norrina B Allen ◽  
Mercedes R Carnethon ◽  
...  
Keyword(s):  
Population Attributable Fraction ◽  
Population Level ◽  
Attributable Fraction ◽  
Incident Diabetes ◽  
Obesity Prevalence ◽  
Longitudinal Assessment ◽  
Prevalence Of Obesity ◽  
Race Ethnicity ◽  
Nationally Representative ◽  
Over Time

Introduction: Estimating effects of population-level increases in obesity on diabetes has important implications for public health policy. Therefore, we used the population attributable fraction (PAF) to quantify trends in the burden of obesity on incident diabetes in key sex and race/ethnicity subgroups over time. Hypothesis: PAFs for obesity attributable incident diabetes will be greatest for women, differ by race/ethnicity and increase over time. Methods: Using data from the Multi-Ethnic Study of Atherosclerosis (MESA), we estimated unadjusted and adjusted hazard ratios (HRs) of obesity on incident diabetes mellitus (DM). We included non-Hispanic White (NHW), non-Hispanic Black (NHB) and Hispanic-Mexican (MA) MESA participants with available data on body mass index and key covariates and without DM at baseline. Next, we estimated the prevalence of obesity in four pooled groups of NHANES survey cycles from 2001-2016 with similar characteristics to MESA participants (age 45-80 years, NHW, NHB, and MA without cardiovascular disease). Lastly, we combined estimates from MESA and NHANES to quantify unadjusted and adjusted PAFs (adjusted for age, income, education, physical activity and diet). Leveraging the objective longitudinal assessment of incident DM in MESA and a nationally representative sample of NHANES, we estimated broadly generalizable PAF estimates over time. Results: Of 3869 MESA participants, mean age was 61 (± 10) years with 47% men, 56% NHW, 31% NHB, and 13% MA. Incidence of DM was 11% with overall adjusted HR of 2.75 (95% CI 2.26, 3.34) for obesity. Prevalence of obesity increased from 34% (32, 37) in 2001-2004 to 41% (39, 44) in 2013-2016. Overall adjusted PAFs ranged from 0.23 to 0.52 over the study period (TABLE), with greatest estimates in NHW women. Conclusions: In conclusion, over time, the proportion of diabetes due to obesity has increased across multiple subgroups of the population, further highlighting the health burden of obesity in the population.

Race at Work

2019 ◽  
pp. 21-65
Author(s):  
Henrice Altink
Keyword(s):  
Public Sector ◽  
Labour Market ◽  
Census Data ◽  
Economic Factors ◽  
The Public ◽  
Political Partisanship ◽  
Changes Over Time ◽  
Over Time

Making extensive use of census data, this chapter sets out changes over time in the race and colour profile of the labour market. It shows that dark-skinned Jamaicans made considerable advances, especially in the public sector, but that even long after independence they were still largely absent from some fields and in others rarely found at senior levels. It will be argued that the stratification of the labour market by colour was largely the result of race-neutral practices, such as educational qualifications and other hiring and promotion criteria; disadvantage accumulated over time and across racial domains; and government inaction, which was partly triggered by political partisanship and economic factors.

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