CopyConfirmHospCuDeathAgeGender

Includes number of total confirmed positive cases in San Diego as posted by https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs/community_epidemiology/dc/2019-nCoV/status.html.

County of San Diego confirmed COVID cases by zip code. NO LONGER UPDATED. Updated dataset can be found: https://data.sandiegocounty.gov/dataset/COVID-19-Statistics-by-Zip-Code/jtds-js8h

This is the daily information that are used in the public CoVID-19 Surveillance, Trends, and Progress and Warnings Dashboards. Each field is updated after 6pm CST Monday through Friday. Weekend data is added on Monday as individual records, along with Monday's reported data. The Surveillance Dashboard is live and available here.Backlog CoVID-19 cases are cases that are reported more than 14-days after the event date (date of Test or date of onset of symptoms). Backlog cases are reported along with the Monday Cumulative Cases, but are not included in in the daily Case Change.This data reflects information provided by the City of San Antonio Metro Health Department, and is released Monday through Friday at 6PM on the City of San Antonio CoVID-19 website.

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This project analyzed the increase of food delivery services and Covid exposure risk correlations. It used geoenrichment to create a feature layer in ArcGIS for each business, created a feature layer for the Covid Case rate by ZIP code, and applied spatial join to generate a 'risk' level for each business for analysis.Additional information in the Project PDFNotable Modules Used: Python: pandas, numpy, matplotlib ArcGIS: enrich, BufferStudyArea

TO DOWNLOAD THE DATASET, CLICK ON THE "Download" BUTTON

Data by medical encounter for the following conditions by age, race/ethnicity, and gender:

Assaults Disorders of the Teeth and Jaw Drowning Falls Firearm-Related Injuries
Heat-Related Illnesses and Injuries
Hip Fractures
Homicide (See Assault Death)
Injuries
Motor Vehicle Injuries
Motor Vehicle Injuries to Pedalcyclist
Motor Vehicle Injuries to Pedestrian
Poisoning
Unintentional Injuries

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.
Blank Cells: Rates not calculated for fewer than 11 events. Rates not calculated in cases where zip code is unknown. Geography not reported where there are no cases reported in a given year. SES: Is the median household income by SRA community. Data for SRAs only.
*The COVID-19 pandemic was associated with increases in all-cause mortality. COVID-19 deaths have affected the patterns of mortality including those of Injury conditions.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS). California Department of Health Care Access and Information (HCAI), Emergency Department Database and Patient Discharge Database, 2020. SANDAG Population Estimates, 2020 (vintage: 09/2022). Population estimates were derived using the 2010 Census and data should be considered preliminary. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, February 2023.

2020 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2020CommunityProfilesDataGuideandDataDictionaryDashboard_16763944288860/HomePage

MATERIALS AND METHODSStudy population: We enrolled 325 consecutive children under the age of 18 years who sought medical care at Penn State Children’s Hospital during the COVID-19 pandemic (early 2020 to late 2021) and were found to have a positive COVID-19 PCR test. Children with a lack of informed consent from the parents were excluded. Blood and plasma samples were available from 325 and 194 children, respectively.Classification of subjects: Controls were defined as those with mild disease if they did not require hospitalization due to COVID-19 or were hospitalized for a reason other than COVID-19. Cases were defined as subjects with severe disease if they were admitted to either general inpatient wards or the intensive care unit (ICU). Clinical and demographic data were collected from all subjects’ medical records, including age, weight, sex, race, ethnicity, comorbidities, exposure history, visit type, hospitalization status, environmental exposures, co-infections, antibiotic use, respiratory support requirements and duration, use of extracorporeal membrane oxygen (ECMO), and treatments used (shown in Table 1).Plasma surfactant protein concentration: Plasma levels of SP-A, SP-B, SP-C and SP-D were measured using Enzyme linked immunosorbent assays (ELISA) kits, following manufacturer’s recommendations (Novus Biologicals, LLC, Centennial, CO, USA for SP-A and SP-B, Biomatik Corporations, Wilmington, DE, USA; and Invitrogen, Life Technologies Corporation, Carlsbad, CA, USA for SP-D). The samples were tested in duplicates and accepted with a coefficient of variation of 5%. SP-C levels could not be measured due to their very low concentrations in the plasma samples.DNA isolation: DNA was extracted from blood samples using the QIAamp Blood Kit (Qiagen, Valencia, CA, USA) as described in the manufacturer’s instructions (L. C. Depicolzuane et al., 2022; DiAngelo et al., 1999).Genotyping: A multiplexed polymerase chain reaction (PCR) workflow of Ampliseq using custom designed panels from Illumina (Illumina, San Diego, CA) was used to analyze the SFTPA1, SFTPA2, SFTPB, SFTPC, and SFTPD genes (L. C. Depicolzuane et al., 2022). The data processing are described in detailed here (L. C. Depicolzuane et al., 2022). The genotypes of SP-A1 (6A, 6Am, m=0-13) and SP-A2 (1A, 1An, n=0-15) were assigned as indicated by DiAngelo et al. (1999).Statistical analysis: All variables were summarized prior to analysis to assess their distributions. Demographic variables were compared between mild and severe cases using a two-sample t-test for continuous variables and a Chi-square test for categorical variables. A quantile regression model was used to compare the median SP-A, SP-B, and SP-D between mild and severe cases unadjusted for covariates and adjusted for age, co-viral, and co-bacterial infections. The correlation between SP levels and age was tested using Spearman correlation both overall and within each severity group. A receiver operating curve (ROC) analysis was applied to determine the optimal cut point for SP-A as a predictor of severe disease. The Youden Index was used to find the cut point where the sensitivity and specificity were maximum simultaneously. A binary predictor for each SP level was created and included in a logistic regression model as a predictor of the severity while adjusting for sex and history of asthma. Odds ratios (ORs) are used to quantify the magnitude and direction of any significant associations. All analyses were performed using SAS software version 9.4 (SAS Instituted, Cary, NC) and a type 1 error rate of 0.05.Genetic association analysis: To investigate the association between SP genetic variants and COVID-19 severity, we conducted logistic regression analyses for each SNP of interest using PLINK 2.0, with COVID-19 severity as the primary outcome. The models were adjusted for key confounders, including age, sex, and race. In these models, sex was coded as a binary variable, with males serving as the reference group. Race was represented by a series of dummy variables for the following categories: Patient declined (0), White (1), Black (2), Asian (3), Mixed (4), Other (5), and Native Hawaiian or Pacific Islander (6).Results for each SNP were expressed as ORs with corresponding p-values. To ensure the robustness of our findings, statistical significance was determined using the Bonferroni correction to account for multiple comparisons across all SNPs and SP-A genotypes analyzed. This correction was applied using R version 4.3.3.Ethical consideration: The study was approved by the Human Subjects Protection Office of The Pennsylvania State University College of Medicine. Informed consent was obtained from the parent or guardian of each subject. All blood and plasma samples were assigned numbers upon arrival with no additional identifiers. To minimize bias, those measuring plasma SP levels and performing DNA extraction and genotyping were blinded to the sample identities.

Virulent infectious agents such as SARS-CoV-2 and Methicillin Resistant Staphylococcus Aureus (MRSA) induce tissue damage that recruits neutrophils and monocyte/macrophages that promote T cell exhaustion, fibrosis, vascular leak, epithelial cell depletion, and fatal organ damage. Neutrophils and macrophages recruited to pathogen infected lungs, including SARS-CoV-2 infected lungs, express phosphatidylinositol 3-kinase gamma (PI3Kg), a signaling protein that coordinately controls granulocyte and monocyte trafficking to diseased tissues and immune suppressive, pro-fibrotic transcription in myeloid cells. PI3Kg deletion and inhibition with the clinical PI3Kg inhibitor eganelisib promoted survival in models of infectious diseases, including SARS-CoV-2 and MRSA, by suppressing inflammation, vascular leak, organ damage and cytokine storm. These results demonstrate essential roles for PI3Kg in inflammatory lung disease and support the potential use of PI3Kg inhibitors to suppress inflammation ..., Human subjects All human tissue analyses were conducted on de-identified tissue under guidelines established by the Institutional Review Board for human subject research of the University of California, San Diego. Postmortem tissue studies received IRB exemption from oversight as research on deceased patients is not classified as human subjects research by the US Department of Health and Human Services or the US Food and Drug Administration. All patients in this study were admitted early in the pandemic during the first 3-6 months after the first cases of COVID-19 were reported in the US when no specific therapeutics had been developed. De-identified lung tissue was obtained upon rapid autopsy of deceased COVID-19 patients formalin-fixed for 48h and processed by the Department of Pathology, UCSD, into paraffin-embedded tissue blocks by the Moores Cancer Center histology shared resource, UCSD, San Diego, CA. Normal human lung tissue was obtained from consented patients during lung cancer..., Any spreadsheet program such as Excel., # PI3Kg inhibition circumvents inflammation and mortality in SARS-CoV-2 and other infections

• File name: README_Dataset-PI3KgCOVIDproject.txt

• Authors: Judith A. Varner

• Other contributors: Ryan M. Shepard, Anghesom Ghebremedhin, Isa Pratumchai, Sally Robinson, Courtney Betts, Jingjing Hu, Roman Sasik, Kathleen M. Fisch, Jaroslav Zak, Hui Chen, Marc Paradise, Mohammad Amjad, Jason Rivera, Satoshi Uchiyama, Hideya Seo, Alex Campos, Denise Ann Dayao, Saul Tzipori, Cesar Piedra-Mora, Soumita Das, Farnaz Hasteh, Hana Russo, Xin Sun, Le Xu, Laura Crotty Alexander, Jason M. Duran, Mazen Odish, Victor Pretorius, Nell C. Kirchberger, Shao-ming Chin, Tami Von Schalscha, David Cheresh, John D. Morrey, Rossitza Alargova, Brenda O’Connell, Theodore A. Martinot, Sandip P. Patel, Victor Nizet, Amanda J. Martinot, Lisa M. Coussens, John Teijaro

• Date created: 2024-03-14

Dataset Version and Release History

• Current Version:
  • Number: 1.0.0
  • Date: 2024-03-14 *...

Data by medical encounter for the following conditions by age, race/ethnicity, and gender:

Influenza (Flu) Flu/Pneumonia Pneumonia Urinary Tract Infections

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.
Blank Cells: Rates not calculated for fewer than 11 events. Rates not calculated in cases where zip code is unknown. Geography not reported where there are no cases reported in a given year. SES: Is the median household income by SRA community. Data for SRAs only.
*The COVID-19 pandemic was associated with increases in all-cause mortality. COVID-19 deaths have affected the patterns of mortality including those of Communicable conditions.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS). California Department of Health Care Access and Information (HCAI), Emergency Department Database and Patient Discharge Database, 2020. SANDAG Population Estimates, 2020 (vintage: 09/2022). Population estimates were derived using the 2010 Census and data should be considered preliminary. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, February 2023.

2020 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2020CommunityProfilesDataGuideandDataDictionaryDashboard_16763944288860/HomePage

TO DOWNLOAD THE DATASET, CLICK ON THE "Download" BUTTONCoVID-19 Cases and Deaths reported weekly grouped by Race/Ethnicity. This data is contains the data reported on Monday going back to March 23rd, the first date available for the data. The Attribute fields are either Race non-Hispanic/Latino or Hispanic/Latino. All people of a specific Race that identify as Hispanic/Latino fall into that category in the data. The counts in each record are cumulative up to the date of the record.This data is a product of CoVID-19+ case management, maintained by the San Antonio Metropolitan Health District.

Data by medical encounter for the following conditions by age, race/ethnicity, and gender:

Alcohol Poisoning
Alcohol Related Disorders
Anxiety and Fear Related Disorders
Cannabis Overdoses
Cannabis Related Disorders
Depression
Impulse and Conduct Disorders
Miscellaneous Mental Health Disorders
Mood Disorders
Neurodevelopmental Disorders
Opioid Overdoses
Opioid Related Disorders
Personality Disorders
Schizophrenia
Sedative Overdoses
Sedative Related Disorders
Stimulant Overdoses
Stimulant Related Disorders
Substance Related Disorders
Suicide
Trauma and Stressor Related Disorders

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.
Blank Cells: Rates not calculated for fewer than 11 events. Rates not calculated in cases where zip code is unknown. Geography not reported where there are no cases reported in a given year. SES: Is the median household income by SRA community. Data for SRAs only.

*The COVID-19 pandemic was associated with increases in all-cause mortality. COVID-19 deaths have affected the patterns of mortality including those of Behavioral Health conditions.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS). California Department of Health Care Access and Information (HCAI), Emergency Department Database and Patient Discharge Database, 2020. SANDAG Population Estimates, 2020 (vintage: 09/2022). Population estimates were derived using the 2010 Census and data should be considered preliminary. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, February 2023.

2020 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2020CommunityProfilesDataGuideandDataDictionaryDashboard_16763944288860/HomePage

Multisystem inflammatory syndrome in children (MIS-C) is a severe, post-infectious sequela of SARS-CoV-2 infection, yet the pathophysiological mechanism connecting the infection to the broad inflammatory syndrome remains unknown. Here we leveraged a large set of MIS-C patient samples (n=199) to identify a distinct set of host proteins that are differentially targeted by patient autoantibodies relative to matched controls. We identified an autoreactive epitope within SNX8, a protein expressed primarily in immune cells that regulates an antiviral pathway associated with MIS-C pathogenesis. In parallel, we also probed the SARS-CoV-2 proteome-wide MIS-C patient antibody response and found it to be differentially reactive to a distinct domain of the SARS-CoV-2 nucleocapsid (N) protein relative to controls. This viral N region and the mapped SNX8 epitope bear remarkable biochemical similarity. Furthermore, we find that many children with anti-SNX8 autoantibodies also have T cells cross-reactive to both SNX8 and this distinct region of the SARS-CoV-2 N protein. Together, these findings suggest that MIS-C patients develop a characteristic immune response against the SARS-CoV-2 N protein that is associated with cross-reactivity to the self-protein SNX8, demonstrating a mechanistic link from the infection to the inflammatory syndrome with implications for better understanding a range of post-infectious autoinflammatory diseases. Methods Patients Patients were recruited through the prospectively enrolling multicenter Overcoming COVID-19 and Taking on COVID-19 Together study in the United States. The study was approved by the central Boston Children’s Hospital Institutional Review Board (IRB) and reviewed by IRBs of participating sites with CDC IRB reliance. A total of 292 patients were enrolled into 1 of the following independent cohorts between June 1, 2020 and September 9, 2021: 223 patients hospitalized with MIS-C (199 in the primary discovery cohort, 24 in a separate subsequent validation cohort), 29 patients hospitalized for COVID-19 in either an intensive care or step-down unit (referred to as severe acute COVID-19 in this study), and 45 outpatients (referred to as “at-risk controls” in this study) post-SARS-CoV-2 infections associated with mild or no symptoms. The demographic and clinical data are summarized in Table I, Extended Data Table 1, and Extended Data Table 2. The 2020 US Centers for Disease Control and Prevention case definition was used to define MIS-C(1). All patients with MIS-C had positive SARS-CoV-2 serology results and/or positive SARS-CoV-2 test results by reverse transcriptase quantitative PCR. All patients with severe COVID-19 or outpatient SARS-CoV-2 infections had a positive antigen test or nucleic acid amplification test for SARS-CoV-2. For outpatients, samples were collected from 36 to 190 days after the positive test (median, 70 days after the positive test; interquartile range, 56-81 days). For use as controls in the SARS-CoV-2 specific PhIP-Seq, plasma from 48 healthy, pre-COVID-19 controls was obtained as deidentified samples from the New York Blood Center. These samples were part of retention tubes collected at the time of blood donations from volunteer donors who provided informed consent for their samples to be used for research. Human proteome PhIP-Seq Human Proteome PhIP-Seq was performed following our previously published vacuum-based PhIP-Seq protocol (2) (https://www.protocols.io/view/scaled-high-throughput-vacuum-phip-protocol-ewov1459kvr2/v1). Our human peptidome library consists of a custom-designed phage library of 731,724 unique T7 bacteriophage each presenting a different 49 amino-acid peptide on its surface. Collectively these peptides tile the entire human proteome including all known isoforms (as of 2016) with 25 amino-acid overlaps. 1 milliliter of phage library was incubated with 1 microliter of human serum overnight at 4C and immunoprecipitated with 25 microliters of 1:1 mixed protein A and protein G magnetic beads (Thermo Fisher, Waltham, MA, #10008D and #10009D). These beads were then washed, and the remaining phage-antibody complexes were eluted in 1 milliliter of E.Coli (BLT5403, EMD Millipore, Burlington, MA) at 0.5-0.7 OD and amplified by growing in 37C incubator. This new phage library was then re-incubated with the same individual’s serum and the previously described protocol was repeated. DNA was then extracted from the final phage library, barcoded, and PCR-amplified, and Illumina adaptors were added. Next-generation sequencing was then performed using an Illumina sequencer (Illumina, San Diego, CA) to a read depth of approximately 1 million per sample. Human proteome PhIP-Seq analysis All human peptidome analysis (except when specifically stated otherwise) was performed at the gene level, in which all reads for all peptides mapping to the same gene were summed, and 0.5 reads were added to each gene to allow the inclusion of genes with zero reads in mathematical analyses. Within each individual sample, reads were normalized by converting to the percentage of total reads. To normalize each sample against background non-specific binding, a fold-change (FC) over mock-IP was calculated by dividing the sample read percentage for each gene by the mean read percentage of the same gene for the AG bead-only controls. This FC signal was then used for side-by-side comparison between samples and cohorts. FC values were also used to calculate z-scores for each MIS-C patient relative to controls and for each control sample by using all remaining controls. These z-scores were used for the logistic regression feature weighting. In instances of peptide-level analysis, raw reads were normalized by calculating the number of reads per 100,000 reads. SARS-CoV-2 proteome PhIP-Seq SARS-CoV-2 Proteome PhIP-Seq was performed as previously described(3). Briefly, 38 amino acid fragments tiling all open reading frames from SARS-CoV-2, SARS-CoV-1, and 7 other CoVs were expressed on T7 bacteriophage with 19 amino acid overlaps. 1 milliliter of phage library was incubated with 1 microliter of human serum overnight at 4C and immunoprecipitated with 25 microliters of 1:1 mixed protein A and protein G magnetic beads (Thermo Fisher, Waltham, MA, #10008D and #10009D). Beads were washed 5 times on a magnetic plate using a P1000 multichannel pipette. The remaining phage-antibody complexes were eluted in 1 milliliter of E.Coli (BLT5403, EMD Millipore, Burlington, MA) at 0.5-0.7 OD and amplified by growing in a 37°C incubator. This new phage library was then re-incubated with the same individual’s serum and the previously described protocol was repeated for a total of 3 rounds of immunoprecipitations. DNA was then extracted from the final phage library, barcoded, and PCR-amplified, and Illumina adaptors were added. Next-generation sequencing was then performed using an Illumina sequencer (Illumina, San Diego, CA) to a read depth of approximately 1 million per sample. Coronavirus proteome PhIP-Seq analysis To account for differing read depths between samples, the total number of reads for each peptide fragment was converted to the number of reads per 100k (RPK). To calculate normalized enrichment relative to pre-COVID controls (FC > Pre-COVID), the RPK for each peptide fragment within each sample was divided by the mean RPK of each peptide fragment among all pre-COVID controls. These FC > Pre-COVID values were used for all subsequent analyses as described in the text and figures.

HAN Archive - 00432 (2021). https://emergency.cdc.gov/han/2020/han00432.asp. S. E. Vazquez, S. A. Mann, A. Bodansky, A. F. Kung, Z. Quandt, E. M. N. Ferré, N. Landegren, D. Eriksson, P. Bastard, S.-Y. Zhang, J. Liu, A. Mitchell, I. Proekt, D. Yu, C. Mandel-Brehm, C.-Y. Wang, B. Miao, G. Sowa, K. Zorn, A. Y. Chan, V. M. Tagi, C. Shimizu, A. Tremoulet, K. Lynch, M. R. Wilson, O. Kämpe, K. Dobbs, O. M. Delmonte, R. Bacchetta, L. D. Notarangelo, J. C. Burns, J.-L. Casanova, M. S. Lionakis, T. R. Torgerson, M. S. Anderson, J. L. DeRisi, Autoantibody discovery across monogenic, acquired, and COVID-19-associated autoimmunity with scalable PhIP-seq. Elife 11 (2022). C. R. Zamecnik, J. V. Rajan, K. A. Yamauchi, S. A. Mann, R. P. Loudermilk, G. M. Sowa, K. C. Zorn, B. D. Alvarenga, C. Gaebler, M. Caskey, M. Stone, P. J. Norris, W. Gu, C. Y. Chiu, D. Ng, J. R. Byrnes, X. X. Zhou, J. A. Wells, D. F. Robbiani, M. C. Nussenzweig, J. L. DeRisi, M. R. Wilson, ReScan, a Multiplex Diagnostic Pipeline, Pans Human Sera for SARS-CoV-2 Antigens. Cell Rep Med 1, 100123 (2020).

Data by medical encounter for the following conditions by age, race/ethnicity, and gender: Congenital Anomalies Maternal Complications

Visit https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs/maternal_child_family_health_services/MCFHSstatistics.html to view MCFHS perinatal health indicators, including: Live Births Low Birth Weight Teen Births Fetal Mortality Early Prenatal Care Infant Mortality Preterm Birth Maternal Deaths

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population. Blank Cells: Rates not calculated for fewer than 11 events. Rates not calculated in cases where zip code is unknown. Geography not reported where there are no cases reported in a given year. SES: Is the median household income by SRA community. Data for SRAs only. *The COVID-19 pandemic was associated with increases in all-cause mortality. COVID-19 deaths have affected the patterns of mortality including those of Maternal Child Health conditions.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS). California Department of Health Care Access and Information (HCAI), Emergency Department Database and Patient Discharge Database, 2020. SANDAG Population Estimates, 2020 (vintage: 09/2022). Population estimates were derived using the 2010 Census and data should be considered preliminary. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, February 2023.

2020 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2020CommunityProfilesDataGuideandDataDictionaryDashboard_16763944288860/HomePage