Characteristics of COVID-19 Cases and Outbreaks at Child Care Facilities - District of Columbia, July-December 2020.

The occurrence of cases of COVID-19 reported by child care facilities among children, teachers, and staff members is correlated with the level of community spread (1,2). To describe characteristics of COVID-19 cases at child care facilities and facility adherence to guidance and recommendations, the District of Columbia (DC) Department of Health (DC Health) and CDC reviewed COVID-19 case reports associated with child care facilities submitted to DC Health and publicly available data from the DC Office of the State Superintendent of Education (OSSE) during July 1-December 31, 2020. Among 469 licensed child care facilities, 112 (23.9%) submitted 269 reports documenting 316 laboratory-confirmed cases and three additional cases identified through DC Health's contact tracers. Outbreaks associated with child care facilities,† defined as two or more laboratory-confirmed and epidemiologically linked cases at a facility within a 14-day period (3), occurred in 27 (5.8%) facilities and accounted for nearly one half (156; 48.9%) of total cases. Among the 319 total cases, 180 (56.4%) were among teachers or staff members. The majority (56.4%) of facilities reported cases to DC Health on the same day that they were notified of a positive test result for SARS-CoV-2, the virus that causes COVID-19, by staff members or parents.§ Facilities were at increased risk for an outbreak if they had been operating for <3 years, if symptomatic persons sought testing ≥3 days after symptom onset, or if persons with asymptomatic COVID-19 were at the facility. The number of outbreaks associated with child care facilities was limited. Continued implementation and maintenance of multiple prevention strategies, including vaccination, masking, physical distancing, cohorting, screening, and reporting, are important to reduce transmission of SARS-CoV-2 in child care facilities and to facilitate a timely public health response to prevent outbreaks.¶.

Investigation of Salmonella Enteritidis Outbreak Associated with Truffle Oil - District of Columbia, 2015.

On September 8, 2015, the District of Columbia Department of Health (DCDOH) received a call from a person who reported experiencing gastrointestinal illness after eating at a District of Columbia (DC) restaurant with multiple locations throughout the United States (restaurant A). Later the same day, a local emergency department notified DCDOH to report four persons with gastrointestinal illness, all of whom had eaten at restaurant A during August 30-September 5. Two patients had laboratory-confirmed Salmonella group D by stool culture. On the evening of September 9, a local newspaper article highlighted a possible outbreak associated with restaurant A. Investigation of the outbreak by DCDOH identified 159 patrons who were residents of 11 states and DC with gastrointestinal illness after eating at restaurant A during July 1-September 10. A case-control study was conducted, which suggested truffle oil-containing food items as a possible source of Salmonella enterica serotype Enteritidis infection. Although several violations were noted during the restaurant inspections, the environmental, laboratory, and traceback investigations did not confirm the contamination source. Because of concern about the outbreak, the restaurant's license was suspended during September 10-15. The collaboration and cooperation of the public, media, health care providers, and local, state, and federal public health officials facilitated recognition of this outbreak involving a pathogen commonly implicated in foodborne illness.

Predicting COVID-19 and respiratory illness: results of the 2022-2023 Armed Forces Health Surveillance Division forecasting challenge.

Since 2019, the Integrated Biosurveillance Branch of the Armed Forces Health Surveillance Division has conducted an annual forecasting challenge during influenza season to predict short-term respiratory disease activity among Military Health System beneficiaries. Weekly case and encounter observed data were used to generate 1- through 4-week advanced forecasts of disease activity. To create unified combinations of model inputs for evaluation across multiple spatial resolutions, 8 individual models were used to calculate 3 ensemble models. Forecast accuracy compared to the observed activity for each model was evaluated by calculating a weighted interval score. Weekly 1- through 4-week ahead forecasts for each ensemble model were generally higher than observed data, especially during periods of peak activity, with peaks in forecasted activity occurring later than observed peaks. The larger the forecasting horizon, the more pronounced the gap between forecasted peak and observed peak. The results showed that several models accurately predicted COVID-19 cases and respiratory encounters with enough lead time for public health response by senior leaders.

Enhanced One Health Surveillance during the 58th Presidential Inauguration-District of Columbia, January 2017.

OBJECTIVE: In January 2017, Washington, DC, hosted the 58th United States presidential inauguration. The DC Department of Health leveraged multiple health surveillance approaches, including syndromic surveillance (human and animal) and medical aid station-based patient tracking, to detect disease and injury associated with this mass gathering. METHODS: Patient data were collected from a regional syndromic surveillance system, medical aid stations, and an internet-based emergency department reporting system. Animal health data were collected from DC veterinary facilities. RESULTS: Of 174 703 chief complaints from human syndromic data, there were 6 inauguration-related alerts. Inauguration attendees who visited aid stations (n = 162) and emergency departments (n = 180) most commonly reported feeling faint/dizzy (n = 29; 17.9%) and pain/cramps (n = 34;18.9%). In animals, of 533 clinical signs reported, most were gastrointestinal (n = 237; 44.5%) and occurred in canines (n = 374; 70.2%). Ten animals that presented dead on arrival were investigated; no significant threats were identified. CONCLUSION: Use of multiple surveillance systems allowed for near-real-time detection and monitoring of disease and injury syndromes in humans and domestic animals potentially associated with inaugural events and in local health care systems.

Re-evaluation of model-based light-scattering spectroscopy for tissue spectroscopy.

Model-based light scattering spectroscopy (LSS) seemed a promising technique for in-vivo diagnosis of dysplasia in multiple organs. In the studies, the residual spectrum, the difference between the observed and modeled diffuse reflectance spectra, was attributed to single elastic light scattering from epithelial nuclei, and diagnostic information due to nuclear changes was extracted from it. We show that this picture is incorrect. The actual single scattering signal arising from epithelial nuclei is much smaller than the previously computed residual spectrum, and does not have the wavelength dependence characteristic of Mie scattering. Rather, the residual spectrum largely arises from assuming a uniform hemoglobin distribution. In fact, hemoglobin is packaged in blood vessels, which alters the reflectance. When we include vessel packaging, which accounts for an inhomogeneous hemoglobin distribution, in the diffuse reflectance model, the reflectance is modeled more accurately, greatly reducing the amplitude of the residual spectrum. These findings are verified via numerical estimates based on light propagation and Mie theory, tissue phantom experiments, and analysis of published data measured from Barrett's esophagus. In future studies, vessel packaging should be included in the model of diffuse reflectance and use of model-based LSS should be discontinued.

Anatomy-based algorithms for detecting oral cancer using reflectance and fluorescence spectroscopy.

OBJECTIVES: We used reflectance and fluorescence spectroscopy to noninvasively and quantitatively distinguish benign from dysplastic/malignant oral lesions. We designed diagnostic algorithms to account for differences in the spectral properties among anatomic sites (gingiva, buccal mucosa, etc). METHODS: In vivo reflectance and fluorescence spectra were collected from 71 patients with oral lesions. The tissue was then biopsied and the specimen evaluated by histopathology. Quantitative parameters related to tissue morphology and biochemistry were extracted from the spectra. Diagnostic algorithms specific for combinations of sites with similar spectral properties were developed. RESULTS: Discrimination of benign from dysplastic/malignant lesions was most successful when algorithms were designed for individual sites (area under the receiver operator characteristic curve [ROC-AUC],0.75 for the lateral surface of the tongue) and was least accurate when all sites were combined (ROC-AUC, 0.60). The combination of sites with similar spectral properties (floor of mouth and lateral surface of the tongue) yielded an ROC-AUC of 0.71. CONCLUSIONS: Accurate spectroscopic detection of oral disease must account for spectral variations among anatomic sites. Anatomy-based algorithms for single sites or combinations of sites demonstrated good diagnostic performance in distinguishing benign lesions from dysplastic/malignant lesions and consistently performed better than algorithms developed for all sites combined.

Quantitative spectroscopic imaging for non-invasive early cancer detection.

We report a fully quantitative spectroscopy imaging instrument for wide area detection of early cancer (dysplasia). This instrument provides quantitative maps of tissue biochemistry and morphology, making it a potentially powerful surveillance tool for objective early cancer detection. We describe the design, construction, calibration, and first clinical application of this new system. We demonstrate its accuracy using physical tissue models. We validate its diagnostic ability on a resected colon adenoma, and demonstrate feasibility of in vivo imaging in the oral cavity.

Model-based spectroscopic analysis of the oral cavity: impact of anatomy.

In order to evaluate the impact of anatomy on the spectral properties of oral tissue, we used reflectance and fluorescence spectroscopy to characterize nine different anatomic sites. All spectra were collected in vivo from healthy oral mucosa. We analyzed 710 spectra collected from the oral cavity of 79 healthy volunteers. From the spectra, we extracted spectral parameters related to the morphological and biochemical properties of the tissue. The parameter distributions for the nine sites were compared, and we also related the parameters to the physical properties of the tissue site. k-Means cluster analysis was performed to identify sites or groups of sites that showed similar or distinct spectral properties. For the majority of the spectral parameters, certain sites or groups of sites exhibited distinct parameter distributions. Sites that are normally keratinized, most notably the hard palate and gingiva, were distinct from nonkeratinized sites for a number of parameters and frequently clustered together. The considerable degree of spectral contrast (differences in the spectral properties) between anatomic sites was also demonstrated by successfully discriminating between several pairs of sites using only two spectral parameters. We tested whether the 95% confidence interval for the distribution for each parameter, extracted from a subset of the tissue data could correctly characterize a second set of validation data. Excellent classification accuracy was demonstrated. Our results reveal that intrinsic differences in the anatomy of the oral cavity produce significant spectral contrasts between various sites, as reflected in the extracted spectral parameters. This work provides an important foundation for guiding the development of spectroscopic-based diagnostic algorithms for oral cancer.

Nativity as a Determinant of Health Disparities Among Children.

Nativity is not often considered in the study of health disparities. We conducted a cross-sectional, parent-reported survey of demographics, socioeconomic characteristics, healthcare access, and health conditions in New York City schoolchildren (n = 9029). US-born children with US-born parents (US/US) had higher socioeconomic status, better access to healthcare, and reported higher rates of disease diagnoses compared to US-born children with immigrant parents and to immigrant children. Dental cavities were the only condition in which US/US children reported lower prevalence. US/US children had the best healthcare access, most favorable parent-reported health status and highest rate of satisfaction with healthcare. The magnitude of racial/ethnic disparities varied based on nativity of the children being compared. Factors such as the healthy immigrant effect and differential diagnosis rates may explain the results. In conclusion, nativity influences disease burdens and should be considered in health disparities studies.

Raman spectroscopic sensing of carbonate intercalation in breast microcalcifications at stereotactic biopsy.

Microcalcifications are an early mammographic sign of breast cancer and frequent target for stereotactic biopsy. Despite their indisputable value, microcalcifications, particularly of the type II variety that are comprised of calcium hydroxyapatite deposits, remain one of the least understood disease markers. Here we employed Raman spectroscopy to elucidate the relationship between pathogenicity of breast lesions in fresh biopsy cores and composition of type II microcalcifications. Using a chemometric model of chemical-morphological constituents, acquired Raman spectra were translated to characterize chemical makeup of the lesions. We find that increase in carbonate intercalation in the hydroxyapatite lattice can be reliably employed to differentiate benign from malignant lesions, with algorithms based only on carbonate and cytoplasmic protein content exhibiting excellent negative predictive value (93-98%). Our findings highlight the importance of calcium carbonate, an underrated constituent of microcalcifications, as a spectroscopic marker in breast pathology evaluation and pave the way for improved biopsy guidance.

Determinants of breast cancer treatment delay differ for African American and White women.

BACKGROUND: Timeliness of care may contribute to racial disparities in breast cancer mortality. African American women experience greater treatment delay than White women in most, but not all studies. Understanding these disparities is challenging as many studies lack patient-reported data and use administrative data sources that collect limited types of information. We used interview and medical record data from the Carolina Breast Cancer Study (CBCS) to identify determinants of delay and assess whether disparities exist between White and African American women (n = 601). METHODS: The CBCS is a population-based study of North Carolina women. We investigated the association of demographic and socioeconomic characteristics, healthcare access, clinical factors, and measures of emotional and functional well-being with treatment delay. The association of race and selected characteristics with delays of more than 30 days was assessed using logistic regression. RESULTS: Household size, losing a job due to one's diagnosis, and immediate reconstruction were associated with delay in the overall population and among White women. Immediate reconstruction and treatment type were associated with delay among African American women. Racial disparities in treatment delay were not evident in the overall population. In the adjusted models, African American women experienced greater delay than White women for younger age groups: OR, 3.34; 95% confidence interval (CI), 1.07-10.38 for ages 20 to 39 years, and OR, 3.40; 95% CI, 1.76-6.54 for ages 40 to 49 years. CONCLUSIONS: Determinants of treatment delay vary by race. Racial disparities in treatment delay exist among women younger than 50 years. IMPACT: Specific populations need to be targeted when identifying and addressing determinants of treatment delay.

Development and comparative assessment of Raman spectroscopic classification algorithms for lesion discrimination in stereotactic breast biopsies with microcalcifications.

Microcalcifications are an early mammographic sign of breast cancer and a target for stereotactic breast needle biopsy. Here, we develop and compare different approaches for developing Raman classification algorithms to diagnose invasive and in situ breast cancer, fibrocystic change and fibroadenoma that can be associated with microcalcifications. In this study, Raman spectra were acquired from tissue cores obtained from fresh breast biopsies and analyzed using a constituent-based breast model. Diagnostic algorithms based on the breast model fit coefficients were devised using logistic regression, C4.5 decision tree classification, k-nearest neighbor (k -NN) and support vector machine (SVM) analysis, and subjected to leave-one-out cross validation. The best performing algorithm was based on SVM analysis (with radial basis function), which yielded a positive predictive value of 100% and negative predictive value of 96% for cancer diagnosis. Importantly, these results demonstrate that Raman spectroscopy provides adequate diagnostic information for lesion discrimination even in the presence of microcalcifications, which to the best of our knowledge has not been previously reported.

Application of Raman spectroscopy to identify microcalcifications and underlying breast lesions at stereotactic core needle biopsy.

Microcalcifications are a feature of diagnostic significance on a mammogram and a target for stereotactic breast needle biopsy. Here, we report development of a Raman spectroscopy technique to simultaneously identify microcalcification status and diagnose the underlying breast lesion, in real-time, during stereotactic core needle biopsy procedures. Raman spectra were obtained ex vivo from 146 tissue sites from fresh stereotactic breast needle biopsy tissue cores from 33 patients, including 50 normal tissue sites, 77 lesions with microcalcifications, and 19 lesions without microcalcifications, using a compact clinical system. The Raman spectra were modeled on the basis of the breast tissue components, and a support vector machine framework was used to develop a single-step diagnostic algorithm to distinguish normal tissue, fibrocystic change (FCC), fibroadenoma, and breast cancer, in the absence and presence of microcalcifications. This algorithm was subjected to leave-one-site-out cross-validation, yielding a positive predictive value, negative predictive value, sensitivity, and specificity of 100%, 95.6%, 62.5%, and 100% for diagnosis of breast cancer (with or without microcalcifications) and an overall accuracy of 82.2% for classification into specific categories of normal tissue, FCC, fibroadenoma, or breast cancer (with and without microcalcifications). Notably, the majority of breast cancers diagnosed are ductal carcinoma in situ (DCIS), the most common lesion associated with microcalcifications, which could not be diagnosed using previous Raman algorithm(s). Our study shows the potential of Raman spectroscopy to concomitantly detect microcalcifications and diagnose associated lesions, including DCIS, and thus provide real-time feedback to radiologists during such biopsy procedures, reducing nondiagnostic and false-negative biopsies.

Detecting high-grade squamous intraepithelial lesions in the cervix with quantitative spectroscopy and per-patient normalization.

This study develops a spectroscopic algorithm for detection of cervical high grade squamous intraepithelial lesions (HSILs). We collected reflectance and fluorescence spectra with the quantitative spectroscopy probe to measure nine spectroscopic parameters from 43 patients undergoing standard colposcopy with directed biopsy. We found that there is improved accuracy for distinguishing HSIL from non-HSIL (low grade SIL and normal tissue) when we "normalized" spectroscopy parameters by dividing the values extracted from each clinically determined suspicious site by the corresponding value extracted from a clinically normal squamous site from the same patient. The "normalized" scattering parameter (A) at 700nm, best distinguished HSIL from non-HSIL with sensitivity and specificity of 89% and 79% suggesting that a simple, monochromatic instrument measuring only A may accurately detect HSIL.

Effect of anatomy on spectroscopic detection of cervical dysplasia.

It has long been speculated that underlying variations in tissue anatomy affect in vivo spectroscopic measurements. We investigate the effects of cervical anatomy on reflectance and fluorescence spectroscopy to guide the development of a diagnostic algorithm for identifying high-grade squamous intraepithelial lesions (HSILs) free of the confounding effects of anatomy. We use spectroscopy in both contact probe and imaging modes to study patients undergoing either colposcopy or treatment for HSIL. Physical models of light propagation in tissue are used to extract parameters related to tissue morphology and biochemistry. Our results show that the transformation zone, the area in which the vast majority of HSILs are found, is spectroscopically distinct from the adjacent squamous mucosa, and that these anatomical differences can directly influence spectroscopic diagnostic parameters. Specifically, we demonstrate that performance of diagnostic algorithms for identifying HSILs is artificially enhanced when clinically normal squamous sites are included in the statistical analysis of the spectroscopic data. We conclude that underlying differences in tissue anatomy can have a confounding effect on diagnostic spectroscopic parameters and that the common practice of including clinically normal squamous sites in cervical spectroscopy results in artificially improved performance in distinguishing HSILs from clinically suspicious non-HSILs.