IntroductionHerpes zoster ophthalmicus (HZO) occurs due to the reactivation of latent varicella-zoster virus (VZV) and is characterized by the involvement of the ophthalmic branch of the trigeminal nerve. While this pathophysiology is well-established, the precise mechanisms driving VZV reactivation remain incompletely understood. Furthermore, it is unclear whether individuals with common comorbidities that compromise immune function face an elevated risk of developing HZO. Investigating potential links between HZO and chronic systemic conditions holds significant importance from public health, medical, and scientific perspectives. To address these gaps, we conducted a study to examine the association between HZO development, sociodemographic factors, and systemic comorbidities.Materials and methodsThis nationwide, population-based, retrospective, matched case-controlled study included 52,112 patients with HZO (identified by the International Classification of Diseases, Ninth Revision, Clinical Modification code 053.2 for herpes zoster with ophthalmic complications) from the Taiwan National Health Insurance Research Database. The age-, sex-, and index date-matched control group included 52,112 non-HZO individuals from the Taiwan Longitudinal Health Insurance Database 2000. Sociodemographic factors and associated systemic diseases were examined using univariate logistic regression analyses, and continuous variables were analysed using paired t-tests. The odds ratios (ORs) for developing HZO were compared using adjusted logistic regression analysis.ResultsPatients with systemic diseases (hypertension, diabetes, hyperlipidaemia, etc.) had significantly higher ORs for HZO development. Patients whose monthly income was >NT$ 30,000 and patients residing in southern Taiwan had increased odds of developing HZO; however, patients residing in northern Taiwan, metropolitans, or satellite cities, and being public servants (military, civil, teaching staff, etc.) had decreased odds of developing HZO.DiscussionHZO is strongly associated with hypertension, diabetes mellitus, hyperlipidaemia, coronary artery disease, chronic renal disease, and human immunodeficiency virus infection. These findings emphasise the role of systemic health in HZO risk.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

Profiles of six high risk malaria clusters based on individual risk factors, including select class conditional probabilities (full in S3 Table) and key socio-demographic and behavioral indicators.

China COVID-19: Confirmed Case: Severe Case: Preexisting Illnesses Combined with COVID-19 data was reported at 16.000 Person in 27 Apr 2023. This records an increase from the previous number of 8.000 Person for 20 Apr 2023. China COVID-19: Confirmed Case: Severe Case: Preexisting Illnesses Combined with COVID-19 data is updated daily, averaging 8.000 Person from Jan 2023 (Median) to 27 Apr 2023, with 15 observations. The data reached an all-time high of 96,661.000 Person in 12 Jan 2023 and a record low of 4.000 Person in 13 Apr 2023. China COVID-19: Confirmed Case: Severe Case: Preexisting Illnesses Combined with COVID-19 data remains active status in CEIC and is reported by Chinese Center for Disease Control and Prevention. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GZ: COVID-19: No of Patient.

China COVID-19: Confirmed Case: Severe Case: COVID-19 data was reported at 3.000 Person in 27 Apr 2023. This records an increase from the previous number of 2.000 Person for 20 Apr 2023. China COVID-19: Confirmed Case: Severe Case: COVID-19 data is updated daily, averaging 2.000 Person from Jan 2023 (Median) to 27 Apr 2023, with 15 observations. The data reached an all-time high of 7,357.000 Person in 12 Jan 2023 and a record low of 0.000 Person in 09 Mar 2023. China COVID-19: Confirmed Case: Severe Case: COVID-19 data remains active status in CEIC and is reported by Chinese Center for Disease Control and Prevention. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GZ: COVID-19: No of Patient.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance publicly available dataset has 33 elements for all COVID-19 cases shared with CDC and includes demographics, geography (county and state of residence), any exposure history, disease severity indicators and outcomes, and presence of any underlying medical conditions and risk behaviors. This dataset requires a registration process and a data use agreement.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

COVID-19 case surveillance data are collected by jurisdictions and are shared voluntarily with CDC. For more information, visit: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.

The deidentified data in the restricted access dataset include demographic characteristics, state and county of residence, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and comorbidities.

All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 case reports have been routinely submitted using standardized case reporting forms.

On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification. All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for lab-confirmed or probable cases.

On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.

Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question "Was the individual hospitalized?" where the possible answer choices include "Yes," "No," or "Unknown," the blank value is recoded to "Missing" because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race, ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<11 COVID-19 case records with a given values). Suppression includes low frequency combinations of case month, geographic characteristics (county and state of residence), and demographic characteristics (sex, age group, race, and ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These and other COVID-19 data are available from multiple public locations:

Note: Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This table summarizes COVID-19 case and death data submitted to CDC as case reports for the line-level dataset. Case and death counts are stratified according to sex, age, and race and ethnicity at regional and national levels. Data for US territories are included in case and death counts, but not population counts. Weekly cumulative counts with five or fewer cases or deaths are not reported to protect confidentiality of patients. Records with unknown or missing sex, age, or race and ethnicity and of multiple, non-Hispanic race and ethnicity are included in case and death totals. COVID-19 case and death data are provisional and are subject to change. Visualization of COVID-19 case and death rate trends by demographic variables may be viewed on COVID Data Tracker (https://covid.cdc.gov/covid-data-tracker/#demographicsovertime).

According to latest figures, the Chinese population decreased by 1.39 million to around 1.408 billion people in 2024. After decades of rapid growth, China arrived at the turning point of its demographic development in 2022, which was earlier than expected. The annual population decrease is estimated to remain at moderate levels until around 2030 but to accelerate thereafter. Population development in China China had for a long time been the country with the largest population worldwide, but according to UN estimates, it has been overtaken by India in 2023. As the population in India is still growing, the country is very likely to remain being home of the largest population on earth in the near future. Due to several mechanisms put into place by the Chinese government as well as changing circumstances in the working and social environment of the Chinese people, population growth has subsided over the past decades, displaying an annual population growth rate of -0.1 percent in 2024. Nevertheless, compared to the world population in total, China held a share of about 17 percent of the overall global population in 2024. China's aging population In terms of demographic developments, the birth control efforts of the Chinese government had considerable effects on the demographic pyramid in China. Upon closer examination of the age distribution, a clear trend of an aging population becomes visible. In order to curb the negative effects of an aging population, the Chinese government abolished the one-child policy in 2015, which had been in effect since 1979, and introduced a three-child policy in May 2021. However, many Chinese parents nowadays are reluctant to have a second or third child, as is the case in most of the developed countries in the world. The number of births in China varied in the years following the abolishment of the one-child policy, but did not increase considerably. Among the reasons most prominent for parents not having more children are the rising living costs and costs for child care, growing work pressure, a growing trend towards self-realization and individualism, and changing social behaviors.

STROBE checklist. (DOC)

*All comparisons by 2-side χ2 test. Date shown in the table is means (±SEM).

39 Ancestry Informative Markers (0.09 MB DOC)

PRISMA 2009 Checklist for this Meta-analysis. (DOC)

Additional file 2: Table S1. Prostate cancer association results for 269 established PCa risk variants from Conti et al. Table S2. Hazard ratio for PCa associated with a 269-SNP PRS. Table S3. Baseline characteristics for General-, EOPC- and LOPC-group from UK Biobank case–control population. Table S4. Odds ratio for PCa associated with a 269-SNP PRS. Table S5. 2,555 Genome-wide significant variants associated with the risk of PCa in the Cox proportional hazard model in UK Biobank. Table S6. 91 Genome-wide significant variants associated with the risk of EOPC in the Cox proportional hazard model in UK Biobank. Table S7. 2500 Genome-wide significant variants associated with the risk of LOPC in the Cox proportional hazard model in UK Biobank. Table S8. 319 independent variants among 1555 Genome-wide significant variants associated with the risk of PCa based on clumping in 1000 Genomes Project. Table S9. 45 independent variants among 223 Genome-wide significant variants associated with the risk of EOPC based on clumping in 1000 Genomes Project. Table S10. 296 independent variants among 1583 Genome-wide significant variants associated with the risk of LOPC based on clumping in 1000 Genomes Project. Table S11. Hazard ratio for PCa associated with the General-, EOPC- and LOPC-PRS based on UK BioBank follow-up cohort. Table S12. Odds ratio for PCa associated with the General-, EOPC- and LOPC-PRS based on UK BioBank case–control population. Table S13. 397 merged SNPs from the 269-SNP PRS and General-PRS. Table S14. 62 merged SNPs from the 269-SNP PRS and EOPC-PRS. Table S15. 375 merged SNPs from the 269-SNP PRS and LOPC-PRS. Table S16. Hazard ratio for PCa associated with the merged-General-, merged-EOPC- and merged-LOPC-PRS based on UK BioBank follow-up cohort. Table S17. Odds ratio for PCa associated with the merged-General-, merged-EOPC- and merged-LOPC-PRS based on UK BioBank case-comtrol population. Table S18. High-confidence traits linked to PCa through two-sample MR analysis. Table S19. High-confidence traits linked to EOPC through two-sample MR analysis. Table S20. High-confidence traits linked to LOPC through two-sample MR analysis.

Gastroschisis case and control sample lifestyle, socio-demographic and nutrition characteristics by individual major stressful life events (Serious relationship difficulties, Legal or financial problems, Victim of violence or crime, Serious illness or injury, Death of someone close). (DOCX)

Distribution of end-digit preference for systolic and diastolic blood pressure. (DOC)

Table S1. Association between mortality and HbA1c. Table S2. Association between mortality and HbA1c, stratified by age group. (PDF)

Additional file 1: Supplementary Material S1. Supplementary Fig. S1. Overall survival rates in the study cohort, Supplementary Fig. S2. Calibration plot for the prognostic model, Supplementary Table S1. Demographic and clinic-pathological characteristics of study population (N = 93), Supplementary Table S2. Platinum plus fluoropyrimidine-based chemotherapy combined treatments used in gastric cancer patients (N = 93), Supplementary Table S3. Grades of toxicity in gastric cancer patients by the Common Toxicity Criteria for Adverse Events (CTCAE) 4.0, Supplementary Table S4. Sex subgroup association analysis of the SNPs DPYD (rs1801265). Supplementary Table S5. Models for hematological grade ≥ 3 toxicity in gastric cancer patients treated with platinum/fluoropyridines -based chemotherapy using multivariate analysis. Supplementary Table S6. Models for gastrointestinal grade ≥ 3 toxicity in gastric cancer patients treated with platinum/fluoropyridines -based chemotherapy using multivariate analysis. Supplementary Table S7. Models for neurological grade ≥ 3 toxicity in gastric cancer patients treated with platinum/fluoropyridines -based chemotherapy using multivariate analysis. Supplementary Table S8. Genotypic and allelic frequencies for the analyzed polymorphisms, Supplementary Table S9. ID assay for each of the analyzed polymorphisms. Supplementary Table S10. SNPs selection based in score for fluoropyrimidines. Supplementary Table S11. Final score for fluoropyrimidines. Supplementary Table S12. SNPs selection based in score for platinums. Supplementary Table S13. Final score for platinums. Supplementary Table S14. Sensitivity, specificity and accuracy calculations from a 2 × 2 confusion matrix. Supplementary Methods: Details of chemotherapy schemes, SNPs selection and classifications algorithm used. Supplementary Data File 1. All raw data used in this study.

July 2016-June 2018.

Table of patient demographics for cases (PBB) and controls.

Supplementary Tables 1-7 Supplementary Table 1. PCR primers and conditions Supplementary Table 2. List of included diagnoses in each histological subgroup Supplementary Table 3. Associations between published genetic risk variants and risk of disease, calculated by conditional logistic regression; Glioma cases and matched controls Supplementary Table 4. Associations between published genetic risk variants and risk of disease, calculated by conditional logistic regression; GBM cases and matched controls Supplementary Table 5. Associations between published genetic risk variants and risk of disease, calculated by conditional logistic regression; Oligodendroglioma cases and matched controls Supplementary Table 6. Associations between published genetic risk variants and risk of disease, calculated by conditional logistic regression; Astrocytoma cases and matched controls Supplementary Table 7. Associations between published genetic risk variants and risk of disease, calculated by conditional logistic regression; Ependymoma cases and matched controls