IntroductionNodding Syndrome (NS), an unexplained illness characterized by spells of head bobbing, has been reported in Sudan and Tanzania, perhaps as early as 1962. Hypothesized causes include sorghum consumption, measles, and onchocerciasis infection. In 2009, a couple thousand cases were reportedly in Northern Uganda.MethodsIn December 2009, we identified cases in Kitgum District. The case definition included persons who were previously developmentally normal who had nodding. Cases, further defined as 5- to 15-years-old with an additional neurological deficit, were matched to village controls to assess risk factors and test biological specimens. Logistic regression models were used to evaluate associations.ResultsSurveillance identified 224 cases; most (95%) were 5–15-years-old (range = 2–27). Cases were reported in Uganda since 1997. The overall prevalence was 12 cases per 1,000 (range by parish = 0·6–46). The case-control investigation (n = 49 case/village control pairs) showed no association between NS and previously reported measles; sorghum was consumed by most subjects. Positive onchocerciasis serology [age-adjusted odds ratio (AOR1) = 14·4 (2·7, 78·3)], exposure to munitions [AOR1 = 13·9 (1·4, 135·3)], and consumption of crushed roots [AOR1 = 5·4 (1·3, 22·1)] were more likely in cases. Vitamin B6 deficiency was present in the majority of cases (84%) and controls (75%).ConclusionNS appears to be increasing in Uganda since 2000 with 2009 parish prevalence as high as 46 cases per 1,000 5- to 15-year old children. Our results found no supporting evidence for many proposed NS risk factors, revealed association with onchocerciasis, which for the first time was examined with serologic testing, and raised nutritional deficiencies and toxic exposures as possible etiologies.

Summary statistics (mean, standard deviation, median, interquartile range, number of subjects) for “ln_adducts” in cases, controls, and total population.

The B.C. COVID-19 Dashboard has been retired and will no longer be updated.Purpose: These data can be used for visual or reference purposes.British Columbia, Canada COVID-19 Regional Summary Date are from the British Columbia Centre for Disease Control, Provincial Health Services Authority and the British Columbia Ministry of Health.

These data represent the British Columbia Health Service Delivery Area and Health Authority 7-day Moving Average COVID-19 case data.

These data were made specifically for the British Columbia COVID-19 Dashboard.

Terms of use, disclaimer and limitation of liabilityAlthough every effort has been made to provide accurate information, the Province of British Columbia, including the British Columbia Centre for Disease Control, the Provincial Health Services Authority and the British Columbia Ministry of Health makes no representation or warranties regarding the accuracy of the information in the dashboard and the associated data, nor will it accept responsibility for errors or omissions. Data may not reflect the current situation, and therefore should only be used for reference purposes. Access to and/or content of these data and associated data may be suspended, discontinued, or altered, in part or in whole, at any time, for any reason, with or without prior notice, at the discretion of the Province of British Columbia.Anyone using this information does so at his or her own risk, and by using such information agrees to indemnify the Province of British Columbia, including the British Columbia Centre for Disease Control, the Provincial Health Services Authority and the British Columbia Ministry of Health and its content providers from any and all liability, loss, injury, damages, costs and expenses (including legal fees and expenses) arising from such person’s use of the information on this website.Dashboard Updates - GeneralData are updated up to the previous Saturday. Weekly metrics reflect the latest full week, Sunday to Saturday. The “Currently Hospitalized” and “Currently in Critical Care” reflect daily volumes on the Thursday.Data Notes - GeneralThe following data notes define the indicators presented on the public dashboard and describe the data sources involved. Data changes as new cases are identified, characteristics of reported cases change or are updated, and data corrections are made. Specific values may therefore fluctuate in response to underlying system changes. As such, case, hospitalization, deaths, testing and vaccination counts and rates may not be directly comparable to previously published reports. For the latest caveats about the data, please refer to the most recent BCCDC Surveillance Report located at: www.bccdc.ca/health-info/diseases-conditions/covid-19/dataData SourcesLaboratory data are supplied by the B.C. Centre for Disease Control (BCCDC) Public Health Laboratory; tests performed for other provinces have been excluded. See “Data Over Time” for more information on changes to the case definition.Total COVID-19 cases include lab-confirmed, lab-probable and epi-linked cases. Case definitions can be found at: https://www.bccdc.ca/health-professionals/clinical-resources/case-definitions/covid-19-(novel-coronavirus). Currently hospitalized and critical care hospitalizations data are received from Provincial COVID-19 Monitoring Solution, Provincial Health Services Authority. See “Data Over Time” for more information on previous data sources.Vaccine data are received from the B.C. Ministry of Health.Mortality data are received from Vital Statistics, B.C. Ministry of Health. See Data Over Time for more information on precious data sources.Laboratory data is supplied by the B.C. Centre for Disease Control Public Health Laboratory and the Provincial Lab Information Solution (PLIS); tests performed for other provinces have been excluded.Critical care hospitalizations are provided by the health authorities to PHSA on a daily basis. BCCDC/PHSA/B.C. Ministry of Health data sources are available at the links below:Cases Totals (spatial)Case DetailsLaboratory Testing InformationRegional Summary DataData Over TimeThe number of laboratory tests performed and positivity rate over time are reported by the date of test result. See “Laboratory Indicators” section for more details.Laboratory confirmed cases are reported based on the client's first positive lab result.As of April 2, 2022, cases include laboratory-diagnosed cases (confirmed and probable) funded under Medical Services Plan.From January 7, 2021 to April 1, 2022, cases included those reported by the health authorities and those with positive laboratory results reported to the BCCDC. The number of cases over time is reported by the result date of the client's first positive lab result where available; otherwise by the date they are reported to public health. Prior to April 2, 2022, total COVID-19 cases included laboratory-diagnosed cases (confirmed and probable) as well as epi-linked cases. Prior to June 4, 2020, the total number of cases included only laboratory-diagnosed cases.As of January 14, 2022, the data source for "Currently Hospitalized" has changed to better reflect hospital capacity. Comparisons to numbers before this date should not be made.As of April 2, 2022, death is defined as an individual who has died from any cause, within 30 days of a first COVID-19 positive lab result date. Prior to April 22, 2022, death information was collected by Regional Health Authorities and defined as any death related to COVID-19. Comparisons between these time periods are not advised.Epidemiologic Indicators"Currently Hospitalized" is the number of people who test positive for COVID-19 through hospital screening practices, regardless of the reason for admission, as recorded in PCMS on the day the dashboard is refreshed. It is reported by the hospital in which the patient is hospitalized, rather than the patient's health authority of residence.Critical care values (intensive care units, high acuity units, and other critical care surge beds) include individuals who test positive for COVID-19 and are in critical care, as recorded in PCMS.The 7-day moving average is an average daily value over the 7 days up to and including the selected date. The 7-day window moved - or changes - with each new day of data. It is used to smooth new daily case and death counts or rates to mitigate the impact of short-term fluctuations and to more clearly identify the most recent trend over time.The following epidemiological indicators are included in the provincial case data file:Date: date of the client's first positive lab result.HA: health authority assigned to the caseSex: the sex of the clientAge_Group: the age group of the clientClassification_Reported: whether the case has been lab-diagnosed or is epidemiologically linked to another caseThe following epidemiological indicators are included in the regional summary data file:Cases_Reported: the number of cases for the health authority (HA) and health service delivery area (HSDA)Cases_Reported_Smoothed: Seven day moving average for reported casesLaboratory IndicatorsTests represent the number of all COVID-19 tests reported to the BCCDC Public Helath Laboratory since testing began mid-January 2020. Only tests for residents of B.C. are included.COVID-19 positivity rate is calculated for each day as the ratio of 7-day rolling average of number of positive specimens to 7-day rolling average of the total number of specimens tested (positive, negative, indeterminate and invalid). A 7-day rolling average applied to all testing data corrects for uneven data release patterns while accurately representing the provincial positivity trends. It avoids misleading daily peaks and valleys due to varying capacities and reporting cadences.Turn-around time is calculated as the daily average time (in hours) between specimen collection and report of a test result. Turn-around time includes the time to ship specimens to the lab; patients who live farther away are expected to have slightly longer average turn around times.The rate of COVID-19 testing per million population is defined as the cumulative number of people tested for COVID-19/B.C. population x 1,000,000. B.C. Please note: the same person may be tested multiple times, thus it is not possible to derive this rate directly from the number of cumulative tests reported on the B.C. COVID-19 Dashboard.Testing context: COVID-19 diagnostic testing and laboratory test guidelines have changed in British Columbia over time. B.C.'s testing strategy has been characterized by four phases: 1) Exposure-based testing (start of pandemic), 2) Targeted testing (March 16, 2020), 3) Expanded testing (April 9, 2020), 4) Symptom-based testing (April 21, 2020), and 5) Symptom-based testing for targeted populations (a-are at risk of more severe disease and/or b-live or work in high-risk settings such as healthcare workers) and Rapid Antigen Tests deployment (January 18, 2022). Due to changes in testing strategies in BC in 2022, focusing on targeted higher risk populations, current case counts are an underestimate of the true number of COVID-19 cases in BC and may not be representative of the situation in the community.
The following laboratory indicators are included in the provincial laboratory data file:New_Tests: the number of new COVID-19 testsTotal_Tests: the total number of COVID-19 testsPositivity: the positivity rate for COVID-19 testsTurn_Around: the turnaround time for COVID-19 testsBC Testing Rate: Total PCR + POC tests per day (excluding POC that were confirmed by PCR within 7 days) / Population using BC Stats PEOPLE2021 population projections for the year 2022 * 100,000.Health Authority AssignmentCases are reported by health authority of residence.As of April 2, 2022, cases are reported based on the address provided at the time of testing; when not available, by location of the provider ordering the lab test.As of April 2, 2022,

Note: The cumulative case count for some counties (with small population) is higher than expected due to the inclusion of non-permanent residents in COVID-19 case counts.

Reporting of Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration. CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• Cases and deaths are based on date of report and not on the date of symptom onset. CDC calculates rates in this data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data were organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts were calculated as the week-to-week change in reported cumulative cases and deaths (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the week before.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues. CDC also worked with jurisdictions after the end of the public health emergency declaration to finalize county data.

• Source: The weekly archived dataset is based on county-level aggregate count data
• Confirmed/Probable Cases/Death breakdown: Cumulative cases and deaths for each county are included. Total reported cases include probable and confirmed cases.
• Time Series Frequency: The weekly archived dataset contains weekly time series data (i.e., one record per week per county)

Important note: The counts reflected during a given time period in this dataset may not match the counts reflected for the same time period in the daily archived dataset noted above. Discrepancies may exist due to differences between county and state COVID-19 case surveillance and reconciliation efforts.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implement these case classifications. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Confirmed and Probable Counts In this dataset, counts by jurisdiction are not displayed by confirmed or probable status. Instead, counts of confirmed and probable cases and deaths are included in the Total Cases and Total Deaths columns, when available. Not all jurisdictions reported probable cases and deaths to CDC. Confirmed and probable case definition criteria are described here: "https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-covid-19/">Coronavirus Disease 2019 (COVID-19) 2023 Case Definition | CDC Council of State and Territorial Epidemiologists (ymaws.com).

Deaths COVID-19 deaths were reported to CDC from several sources since the beginning of the pandemic including aggregate death data and NCHS Provisional Death Counts. Historic information presented on the COVID Data Tracker pages were based on the same source (Aggregate Data) as the present dataset until the expiration of the public health emergency declaration on May 11, 2023; however, the NCHS Death Counts are based on death certificate data that use information reported by physicians, medical examiners, or coroners in the cause-of-death section of each certificate. Counts from previous weeks were continually revised as more records were received and processed.

Number of Jurisdictions Reporting There were 60 public health jurisdictions that reported cases and deaths of COVID-19. This included the 50 states, the District of Columbia, New York City, the U.S. territories of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, Puerto Rico, and the U.S Virgin Islands as well as three independent countries in compacts of free association with the United States, Federated States of Micronesia, Republic of the Marshall Islands, and Republic of Palau. In total there were 3,222 counties for which counts were tracked within the 60 public health jurisdictions.

Additional COVID-19 public use datasets, include line-level (patient-level) data, are available at: https://data.cdc.gov/browse?tags=covid-19.

Note: In early 2020, Alaska enacted changes to their counties/boroughs due to low populations in certain areas:

Case and death counts for Yakutat City and Borough, Alaska, are shown as 0 by default. Case and death counts for Hoonah-Angoon Census Area, Alaska, represent total cases and deaths in residents of Hoonah-Angoon Census Area, Alaska, and Yakutat City and Borough, Alaska. Case and death counts for Bristol Bay Borough, Alaska, are shown as 0 by default. Case and death counts for Lake and Peninsula Borough, Alaska, represent total cases and deaths in residents of Lake and Peninsula Borough, Alaska, and Bristol Bay Borough, Alaska.

Historical cases and deaths are not tracked separately in the county level datasets, and differences in weekly new cases and deaths could exist when county-level data are aggregated to the state-level (i.e., when compared to this dataset: https://data.cdc.gov/Case-Surveillance/United-States-COVID-19-Cases-and-Deaths-by-State-o/9mfq-cb36).

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Updated (Bivalent) Booster Status. Click 'More' for important dataset description and footnotes

Webpage: https://covid.cdc.gov/covid-data-tracker/#rates-by-vaccine-status

Dataset and data visualization details:

These data were posted and archived on May 30, 2023 and reflect cases among persons with a positive specimen collection date through April 22, 2023, and deaths among persons with a positive specimen collection date through April 1, 2023. These data will no longer be updated after May 2023.

Vaccination status: A person vaccinated with at least a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. A person vaccinated with a primary series and a monovalent booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably receiving a primary series of an FDA-authorized or approved vaccine and at least one additional dose of any monovalent FDA-authorized or approved COVID-19 vaccine on or after August 13, 2021. (Note: this definition does not distinguish between vaccine recipients who are immunocompromised and are receiving an additional dose versus those who are not immunocompromised and receiving a booster dose.) A person vaccinated with a primary series and an updated (bivalent) booster dose had SARS-CoV-2 RNA or antigen detected in a respiratory specimen collected ≥14 days after verifiably receiving a primary series of an FDA-authorized or approved vaccine and an additional dose of any bivalent FDA-authorized or approved vaccine COVID-19 vaccine on or after September 1, 2022. (Note: Doses with bivalent doses reported as first or second doses are classified as vaccinated with a bivalent booster dose.) People with primary series or a monovalent booster dose were combined in the “vaccinated without an updated booster” category.

Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Per the interim guidance of the Council of State and Territorial Epidemiologists (CSTE), this should include persons whose death certificate lists COVID-19 disease or SARS-CoV-2 as the underlying cause of death or as a significant condition contributing to death. Rates of COVID-19 deaths by vaccination status are primarily reported based on when the patient was tested for COVID-19. In select jurisdictions, deaths are included that are not laboratory confirmed and are reported based on alternative dates (i.e., onset date for most; or date of death or report date, where onset date is unavailable). Deaths usually occur up to 30 days after COVID-19 diagnosis.

Participating jurisdictions: Currently, these 24 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Colorado, District of Columbia, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (NY), North Carolina, Rhode Island, Tennessee, Texas, Utah, and West Virginia; 23 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 48% of the total U.S. population and all ten of the Health and Human Services Regions. This list will be updated as more jurisdictions participate.

Incidence rate estimates: Weekly age-specific incidence rates by vaccination status were calculated as the number of cases or deaths divided by the number of people vaccinated with a primary series, overall or with/without a booster dose (cumulative) or unvaccinated (obtained by subtracting the cumulative number of people vaccinated with at least a primary series and partially vaccinated people from the 2019 U.S. intercensal population estimates) and multiplied by 100,000. Overall incidence rates were age-standardized using the 2000 U.S. Census standard population. To estimate population counts for ages 6-12 months, half of the single-year population counts for ages <12 months were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred.

Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent incidence and death rates from growing unrealistically large due to potential overestimates of vaccination coverage.

Incidence rate ratios (IRRs): IRRs for the past one month were calculated by dividing the average weekly incidence rates among unvaccinated people by that among people vaccinated without an updated (bivalent) booster dose) or vaccinated with an updated (bivalent) booster dose.

Archive: An archive of historic data, including April 3, 2021-September 24, 2022 and posted on October 21, 2022 is available on data.cdc.gov. The analysis by vaccination status (unvaccinated and at least a primary series) for 31 jurisdictions is posted here: https://data.cdc.gov/Public-Health-Surveillance/Rates-of-COVID-19-Cases-or-Deaths-by-Age-Group-and/3rge-nu2a. The analysis for one booster dose (unvaccinated, primary series only, and at least one booster dose) in 31 jurisdictions is posted here: https://data.cdc.gov/Public-Health-Surveillance/Rates-of-COVID-19-Cases-or-Deaths-by-Age-Group-and/d6p8-wqjm. The analysis for two booster doses (unvaccinated, primary series only, one booster dose, and at least two booster doses) in 28 jurisdictions is posted here: https://data.cdc.gov/Public-Health-Surveillance/Rates-of-COVID-19-Cases-or-Deaths-by-Age-Group-and/ukww-au2k.

References

Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1284–1290.

Johnson AG, Amin AB, Ali AR, et al. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron Variant Emergence — 25 U.S. Jurisdictions, April 4–December 25, 2021. MMWR Morb Mortal Wkly Rep 2022;71:132–138

Johnson AG, Linde L, Ali AR, et al. COVID-19 Incidence and Mortality Among Unvaccinated and Vaccinated Persons Aged ≥12 Years by Receipt of Bivalent Booster Doses and Time Since Vaccination — 24 U.S. Jurisdictions, October 3, 2021–December 24, 2022. MMWR Morb Mortal Wkly Rep 2023;72:145–152

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Argentina in 2006, 2010 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the 2006-2017 Enterprise Survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2006-2017 Argentina Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Manual. Stratified random sampling was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors (group D), construction (group F), services (groups G and H), and transport, storage, and communications (group I). Groups are defined following ISIC revision 3.1. Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, excluding sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors. - To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. - To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)

Three levels of stratification were used in every country: industry, establishment size, and region.

Industry stratification was designed in the following way: In small economies the population was stratified into 3 manufacturing industries, one services industry - retail-, and one residual sector as defined in the sampling manual. Each industry had a target of 120 interviews. In middle size economies the population was stratified into 4 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. For the manufacturing industries sample sizes were inflated by 25% to account for potential non-response in the financing data.

For the Argentina ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposed, the number of employees was defined on the basis of reported permanent full-time workers. This resulted in some difficulties in certain countries where seasonal/casual/part-time labor is common.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies:

a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don't know (-9).

b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from "Don't know" responses.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals; whenever this was done, strict rules were followed to ensure replacements were randomly selected within the same stratum. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Liberia in 2009 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the 2009-2017 Enterprise Survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2009-2017 Liberia Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Note. Stratified random was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except subsector 72, IT, which was added to the population under study), and all public or utilities sectors.

• To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions.
• To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)
• Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous.

• The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

  Three levels of stratification were used in this country: industry, establishment size, and region. Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries. Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72). For the Liberia ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).
  Regional stratification for the Liberia ES was done across three regions: Montserrado, Margibi, and Nimba.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Services and Manufacturing Questionnaire - Screener Questionnaire.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

There was a high response rate especially as a result of positive attitude towards the international community in collaboration with the government in their reconstruction efforts after a period of civil strife.There was also very positive attitude towards World Bank initiatives.

The B.C. COVID-19 Dashboard has been retired and will no longer be updated.Purpose: These data can be used for visual or reference purposes.British Columbia COVID-19 B.C. & Canadian Testing Rates are obtained from the Public Health Agency of Canada’s Daily Epidemiologic Update site: https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html.These data were made specifically for the British Columbia COVID-19 Dashboard.

Terms of use, disclaimer and limitation of liabilityAlthough every effort has been made to provide accurate information, the Province of British Columbia, including the British Columbia Centre for Disease Control, the Provincial Health Services Authority and the British Columbia Ministry of Health makes no representation or warranties regarding the accuracy of the information in the dashboard and the associated data, nor will it accept responsibility for errors or omissions. Data may not reflect the current situation, and therefore should only be used for reference purposes. Access to and/or content of these data and associated data may be suspended, discontinued, or altered, in part or in whole, at any time, for any reason, with or without prior notice, at the discretion of the Province of British Columbia.Anyone using this information does so at his or her own risk, and by using such information agrees to indemnify the Province of British Columbia, including the British Columbia Centre for Disease Control, the Provincial Health Services Authority and the British Columbia Ministry of Health and its content providers from any and all liability, loss, injury, damages, costs and expenses (including legal fees and expenses) arising from such person’s use of the information on this website.Dashboard Updates - GeneralData are updated up to the previous Saturday. Weekly metrics reflect the latest full week, Sunday to Saturday. The “Currently Hospitalized” and “Currently in Critical Care” reflect daily volumes on the Thursday.Data Notes - GeneralThe following data notes define the indicators presented on the public dashboard and describe the data sources involved. Data changes as new cases are identified, characteristics of reported cases change or are updated, and data corrections are made. Specific values may therefore fluctuate in response to underlying system changes. As such, case, hospitalization, deaths, testing and vaccination counts and rates may not be directly comparable to previously published reports. For the latest caveats about the data, please refer to the most recent BCCDC Surveillance Report located at: www.bccdc.ca/health-info/diseases-conditions/covid-19/dataData SourcesLaboratory data are supplied by the B.C. Centre for Disease Control (BCCDC) Public Health Laboratory; tests performed for other provinces have been excluded. See “Data Over Time” for more information on changes to the case definition.Total COVID-19 cases include lab-confirmed, lab-probable and epi-linked cases. Case definitions can be found at: https://www.bccdc.ca/health-professionals/clinical-resources/case-definitions/covid-19-(novel-coronavirus). Currently hospitalized and critical care hospitalizations data are received from Provincial COVID-19 Monitoring Solution, Provincial Health Services Authority. See “Data Over Time” for more information on previous data sources.Vaccine data are received from the B.C. Ministry of Health.Mortality data are received from Vital Statistics, B.C. Ministry of Health. See Data Over Time for more information on precious data sources.Laboratory data is supplied by the B.C. Centre for Disease Control Public Health Laboratory and the Provincial Lab Information Solution (PLIS); tests performed for other provinces have been excluded.Critical care hospitalizations are provided by the health authorities to PHSA on a daily basis. BCCDC/PHSA/B.C. Ministry of Health data sources are available at the links below:Cases Totals (spatial)Case DetailsLaboratory Testing InformationRegional Summary DataData Over TimeThe number of laboratory tests performed and positivity rate over time are reported by the date of test result. See “Laboratory Indicators” section for more details.Laboratory confirmed cases are reported based on the client's first positive lab result.As of April 2, 2022, cases include laboratory-diagnosed cases (confirmed and probable) funded under Medical Services Plan.From January 7, 2021 to April 1, 2022, cases included those reported by the health authorities and those with positive laboratory results reported to the BCCDC. The number of cases over time is reported by the result date of the client's first positive lab result where available; otherwise by the date they are reported to public health. Prior to April 2, 2022, total COVID-19 cases included laboratory-diagnosed cases (confirmed and probable) as well as epi-linked cases. Prior to June 4, 2020, the total number of cases included only laboratory-diagnosed cases.As of January 14, 2022, the data source for "Currently Hospitalized" has changed to better reflect hospital capacity. Comparisons to numbers before this date should not be made.As of April 2, 2022, death is defined as an individual who has died from any cause, within 30 days of a first COVID-19 positive lab result date. Prior to April 22, 2022, death information was collected by Regional Health Authorities and defined as any death related to COVID-19. Comparisons between these time periods are not advised.Epidemiologic Indicators"Currently Hospitalized" is the number of people who test positive for COVID-19 through hospital screening practices, regardless of the reason for admission, as recorded in PCMS on the day the dashboard is refreshed. It is reported by the hospital in which the patient is hospitalized, rather than the patient's health authority of residence.Critical care values (intensive care units, high acuity units, and other critical care surge beds) include individuals who test positive for COVID-19 and are in critical care, as recorded in PCMS.The 7-day moving average is an average daily value over the 7 days up to and including the selected date. The 7-day window moved - or changes - with each new day of data. It is used to smooth new daily case and death counts or rates to mitigate the impact of short-term fluctuations and to more clearly identify the most recent trend over time.The following epidemiological indicators are included in the provincial case data file:Date: date of the client's first positive lab result.HA: health authority assigned to the caseSex: the sex of the clientAge_Group: the age group of the clientClassification_Reported: whether the case has been lab-diagnosed or is epidemiologically linked to another caseThe following epidemiological indicators are included in the regional summary data file:Cases_Reported: the number of cases for the health authority (HA) and health service delivery area (HSDA)Cases_Reported_Smoothed: Seven day moving average for reported casesLaboratory IndicatorsTests represent the number of all COVID-19 tests reported to the BCCDC Public Helath Laboratory since testing began mid-January 2020. Only tests for residents of B.C. are included.COVID-19 positivity rate is calculated for each day as the ratio of 7-day rolling average of number of positive specimens to 7-day rolling average of the total number of specimens tested (positive, negative, indeterminate and invalid). A 7-day rolling average applied to all testing data corrects for uneven data release patterns while accurately representing the provincial positivity trends. It avoids misleading daily peaks and valleys due to varying capacities and reporting cadences.Turn-around time is calculated as the daily average time (in hours) between specimen collection and report of a test result. Turn-around time includes the time to ship specimens to the lab; patients who live farther away are expected to have slightly longer average turn around times.The rate of COVID-19 testing per million population is defined as the cumulative number of people tested for COVID-19/B.C. population x 1,000,000. B.C. Please note: the same person may be tested multiple times, thus it is not possible to derive this rate directly from the number of cumulative tests reported on the B.C. COVID-19 Dashboard.Testing context: COVID-19 diagnostic testing and laboratory test guidelines have changed in British Columbia over time. B.C.'s testing strategy has been characterized by four phases: 1) Exposure-based testing (start of pandemic), 2) Targeted testing (March 16, 2020), 3) Expanded testing (April 9, 2020), 4) Symptom-based testing (April 21, 2020), and 5) Symptom-based testing for targeted populations (a-are at risk of more severe disease and/or b-live or work in high-risk settings such as healthcare workers) and Rapid Antigen Tests deployment (January 18, 2022). Due to changes in testing strategies in BC in 2022, focusing on targeted higher risk populations, current case counts are an underestimate of the true number of COVID-19 cases in BC and may not be representative of the situation in the community.
The following laboratory indicators are included in the provincial laboratory data file:New_Tests: the number of new COVID-19 testsTotal_Tests: the total number of COVID-19 testsPositivity: the positivity rate for COVID-19 testsTurn_Around: the turnaround time for COVID-19 testsBC Testing Rate: Total PCR + POC tests per day (excluding POC that were confirmed by PCR within 7 days) / Population using BC Stats PEOPLE2021 population projections for the year 2022 * 100,000.Health Authority AssignmentCases are reported by health authority of residence.As of April 2, 2022, cases are reported based on the address provided at the time of testing; when not available, by location of the provider ordering the lab test.As of April 2, 2022, cases who reported having an address outside of B.C. are not included.Prior to April 2, 2022, when

∧51 total cases were enrolled; 49 matched to 49 village controls, and 44 matched to 44 household controls.McNemar’s, Stuart’s [27], and paired t tests were performed to obtain significance level.*p

BackgroundIn case-control studies, population controls can help ensure generalizability; however, the selection of population controls can be challenging in environments that lack population registries. We developed a population enumeration and sampling strategy to facilitate use of population controls in a breast cancer case-control study conducted in Ghana.MethodsHousehold enumeration was conducted in 110 census-defined geographic areas within Ghana’s Ashanti, Central, Eastern, and Greater Accra Regions. A pool of potential controls (women aged 18 to 74 years, never diagnosed with breast cancer) was selected from the enumeration using systematic random sampling and frequency-matched to the anticipated distributions of age and residence among cases. Multiple attempts were made to contact potential controls to assess eligibility and arrange for study participation. To increase participation, we implemented a refusal conversion protocol in which initial non-participants were re-approached after several months.Results2,528 women were sampled from the enumeration listing, 2,261 (89%) were successfully contacted, and 2,106 were enrolled (overall recruitment of 83%). 170 women were enrolled through refusal conversion. Compared with women enrolled after being first approached, refusal conversion enrollees were younger and less likely to complete the study interview in the study hospital (13% vs. 23%). The most common reasons for non-participation were lack of interest and lack of time.ConclusionsUsing household enumeration and repeated contacts, we were able to recruit population controls with a high participation rate. Our approach may provide a blue-print for others undertaking epidemiologic studies in populations that lack accessible population registries.

Enriched electronic health records (EHRs) contain crucial information related to disease progression, and this information can help with decision-making in the health care field. Data analytics in health care is deemed as one of the essential processes that help accelerate the progress of clinical research. However, processing and analyzing EHR data are common bottlenecks in health care data analytics. The dxpr R package provides mechanisms for integration, wrangling, and visualization of clinical data, including diagnosis and procedure records. First, the dxpr package helps users transform International Classification of Diseases (ICD) codes to a uniform format. After code format transformation, the dxpr package supports four strategies for grouping clinical diagnostic data. For clinical procedure data, two grouping methods can be chosen. After EHRs are integrated, users can employ a set of flexible built-in querying functions for dividing data into case and control groups by using specified criteria and splitting the data into before and after an event based on the record date. Subsequently, the structure of integrated long data can be converted into wide, analysis-ready data that are suitable for statistical analysis and visualization. We conducted comorbidity data processes based on a cohort of newborns from Medical Information Mart for Intensive Care-III (n = 7,833) by using the dxpr package. We first defined patent ductus arteriosus (PDA) cases as patients who had at least one PDA diagnosis (ICD, Ninth Revision, Clinical Modification [ICD-9-CM] 7470*). Controls were defined as patients who never had PDA diagnosis. In total, 381 and 7,452 patients with and without PDA, respectively, were included in our study population. Then, we grouped the diagnoses into defined comorbidities. Finally, we observed a statistically significant difference in 8 of the 16 comorbidities among patients with and without PDA, including fluid and electrolyte disorders, valvular disease, and others.

Impact of pre-deployment training for healthcare workers on cumulative cases and deaths among overall population caused by EVD.

$Representativeness of cases included all eligible cases with outcome of interest over a defined period of time, all cases in a defined catchment area, all cases in a defined hospital or clinic, group of hospitals, health maintenance organization, or an appropriate sample of those cases (e.g. random sample).#Representativeness of controls assesses whether the control series used in the study is derived from the same population as the cases and essentially would have been cases had the outcome been present and included community and clinical controls within the same community or hospitalized population as cases.&If Yes, the % of the different ethnics groups are provided in brackets.AS: Asiatic. EA: Euro-American; AA: Afro-Americans; Af: African; Eu: Europeans.†HWE: Hardy-Weinberg Equilibrium; ‡TQS: Total Quality Score.

Data set for the ILI and SARI patients enrolled in the NISSS in Tanzania, 2019.

Findings from the evaluation of the National Influenza Sentinel Surveillance System in Tanzania,2019.

Mild Cognitive Impairment (MCI) is a neurological disorder at the transition between normal cognitive decline and dementia. Despite the potential role of neuroinflammation in the pathogenesis of MCI, infectious triggers remain mostly unknown. Infection with Bartonella spp., a zoonotic bacterium, has recently been associated with diffuse neurological and psychiatric symptoms. Given the preferential endothelial localization of Bartonella spp. and the role of vascular changes in neurocognitive decline, we hypothesized that there is an association between Bartonella spp. infection and pathologically accelerated decline in cognitive function in aging. To test this hypothesis, we collected serological and molecular markers of past and present Bartonella spp. infection in a sample of older people with and without MCI. Samples were processed in a blinded way to exclude laboratory biases. Contrary to our hypothesis, people with MCI were not more likely than people without MCI to have an active Bartonella spp. infection as measured by droplet digital PCR (p = 0.735) and quantitative PCR (p = 1). In addition, there was no significant difference in positive serological results between cases and controls (p = 0.461). Overall, higher-than-expected active Bartonella spp. infection (37% by ddPCR) and seroreactivity (71% by indirect fluorescent antibody assay) were found in people without MCI. Conclusions require caution, as our study was limited by the small number of cases with MCI. Overall, our results identified a higher than previously recognized rate of exposure and infection with Bartonella spp. in this older study population but does not support a specific role for such infection in MCI.