Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

Count of COVID-19-associated deaths by date of death. Deaths reported to either the OCME or DPH are included in the COVID-19 data. COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death and persons who were not tested for COVID-19 whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Note the counts in this dataset may vary from the death counts in the other COVID-19-related datasets published on data.ct.gov, where deaths are counted on the date reported rather than the date of death

Mortality Rates for Lake County, Illinois. Explanation of field attributes: Average Age of Death – The average age at which a people in the given zip code die. Cancer Deaths – Cancer deaths refers to individuals who have died of cancer as the underlying cause. This is a rate per 100,000. Heart Disease Related Deaths – Heart Disease Related Deaths refers to individuals who have died of heart disease as the underlying cause. This is a rate per 100,000. COPD Related Deaths – COPD Related Deaths refers to individuals who have died of chronic obstructive pulmonary disease (COPD) as the underlying cause. This is a rate per 100,000.

This dataset contains the number of cases, number of in hospital/30 day deaths, observed, expected and risk- adjusted mortality rates for cardiac surgery and percutaneous coronary interventions (PCI) by hospital. Regions represent where the hospitals are located. The initial Health Data NY dataset includes patients discharged between January 1, 2008, and December 31, 2010. Analyses of risk-adjusted mortality rates and associated risk factors are provided for 2010 and for the three-year period from 2008 through 2010. For PCI, analyses of all cases, non-emergency cases (which represent the majority of procedures) and emergency cases are included. Subsequent year reports data will be appended to this dataset. For more information check out: http://www.health.ny.gov/health_care/consumer_information/cardiac_surgery/ or go to the “About” tab.

This CD-ROM contains the Multiple Cause of Death files for 1968-73. The data collection describes causes of deaths occurring in the United States during 1968-73. For 1972 data year, a 50-percent sample of death records were processed. The data files include underlying causes of death, place of death, whether there were multiple conditions that caused the death, and what those conditions were. In addition, data are provided on date of death, and on sex, race, age, marital status, and origin or descent of the deceased. Also included is information on residence of the deceased (state, county, city, region, and whether the county was a metropolitan or nonmetropolitan area). Data on whether an autopsy was performed and the site of accidents are also provided. Causes of death are coded using The Manual of the International Statistical Classification Of Diseases, Injuries, And Cause-Of-Death, Ninth Revision (ICD-9). Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science at the University of North Carolina in Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items may be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

Check out the PhilaStats Vital Statistics Dashboard for the City of Philadelphia, for interactive maps and charts of vital statistics and trends in natality (births), mortality (deaths), and population for Philadelphia residents. See also the technical notes for the creation and visualization of Philadelphia's Vital Statistics. View metadata for key information about this dataset.Vital statistics are annually published calculations on birth and death records that facilitate the tracking of important health and population trends in Philadelphia over time. Public officials, researchers, and citizens alike may use vital statistics to plan for population shifts and healthcare needs, to perform research, and to stay informed and up-to-date on the natality and mortality trends in our City. The vital statistics dataset consists of natality and mortality data on Philadelphia City residents for each year of finalized data available, back to 2011 for births and 2012 for deaths. Citywide metrics and metrics by Philadelphia Planning District are provided for both natality and mortality metrics. A population estimates table is also provided, which includes the population counts used to calculate some metrics.The Vital Statistics - Population dataset is also available aggregated by planning district and by census tract.For questions about this dataset, contact epi@phila.gov. For technical assistance, email maps@phila.gov.

ABSTRACT: Introduction: The proactive search of deaths is a strategy for capturing events that were not informed to the Mortality Information System of Ministry of Health. Its importance to reduce underreporting of deaths and to evaluate the operation of the information system is widely known. Objective: To describe the methodology and main findings of the Proactive Search of Deaths, 2013, establishing the contribution of different information sources. Methods: The research was carried out in 79 Brazilian municipalities. We investigated several official and unofficial sources of information about deaths of municipality residents. Every information source investigated and all cases found in each source were typed in an on-line panel. The second stage of the research was the confirmation of cases to verify information of year and residence and to complete missing information. For all confirmed cases, we estimated the completeness of death registration and correction factors according to the adequacy level of mortality information. Results: We found 2,265 deaths that were not informed to the Mortality Information System. From those, 49.3% were found in unofficial sources, cemeteries and funeral homes. In some rural municipalities, precarious burial conditions were found in cemeteries in the middle of the forest and no registration of the deceased. Correction factors were inversely associated to the adequacy level of mortality information. Conclusion: The findings confirm the association between level of information adequacy and completeness of death registration, and indicate that the application of the proactive search is an effective method to capture deaths not informed to the Ministry of Health.

This CD-ROM contains the 1981 Mortality Detail Underlying Cause and Multiple Cause of Death files and documentation.

Mortality Detail File: This data collection describes every death registered in the United States during 1981. Information includes the month and day of death, the sex of the deceased, the age of the deceased at the time of death, the deceased's place of residence, place of death, and whether an autopsy was performed. Causes of death are coded using the The International Classification of Diseases (ICD-9).

Multiple Cause of Death File: This data collection describes causes of deaths occurring in the United States during 1981. Items include underlying causes of death, place of death, whether there were multiple conditions that caused the death, and what those conditions were. In addition, data are provided on date of death, and on sex, race, age, marital status, and origin or descent of the deceased. Also included is information on residence of the deceased (state, county, city, region, and whether the county was a metropolitan or nonmetropolitan area). Data on whether an autopsy was performed and the site of accidents are also provided. Causes of death are coded using The Manual of the International Statistical Classification Of Diseases, Injuries, And Cause-Of-Death, Ninth Revision (ICD-9). In 1981, multiple cause data were coded on a 50-percent sample basis for deaths occurring in 19 registration areas. For the remaining 33 registration areas, multiple cause data were processed on a 100-percent basis.

Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science at the University of North Carolina in Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items may be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov. Provisional counts of deaths by the week the deaths occurred, by state of occurrence, and by select underlying causes of death for 2020-2023. The dataset also includes weekly provisional counts of death for COVID-19, coded to ICD-10 code U07.1 as an underlying or multiple cause of death. NOTE: death counts are presented with a one week lag.

BackgroundThere is not enough evidence regarding how information obtained from general health check-ups can predict individual mortality based on long-term follow-ups and large sample sizes. This study evaluated the applicability of various health information and measurements, consisting of self-reported data, anthropometric measurements and laboratory test results, in predicting individual mortality.MethodsThe National Health Screening Cohort included 514,866 participants (aged 40–79 years) who were randomly selected from the overall database of the national health screening program in 2002–2003. Death was determined from causes of death statistics provided by Statistics Korea. We assessed variables that were collected at baseline and repeatedly measured for two consecutive years using traditional and time-variant Cox proportional hazards models in addition to random forest and boosting algorithms to identify predictors of 10-year all-cause mortality. Participants’ age at enrollment, lifestyle factors, anthropometric measurements and laboratory test results were included in the prediction models. We used c-statistics to assess the discriminatory ability of the models, their external validity and the ratio of expected to observed numbers to evaluate model calibration. Eligibility of Medicaid and household income levels were used as inequality indexes.ResultsAfter the follow-up by 2013, 38,031 deaths were identified. The risk score based on the selected health information and measurements achieved a higher discriminatory ability for mortality prediction (c-statistics = 0.832, 0.841, 0.893, and 0.712 for Cox model, time-variant Cox model, random forest and boosting, respectively) than that of the previous studies. The results were externally validated using the community-based cohort data (c-statistics = 0.814).ConclusionsIndividuals’ health information and measurements based on health screening can provide early indicators of their 10-year death risk, which can be useful for health monitoring and related policy decisions.

This CD-ROM contains the 1992 Multiple Cause of Death file and documentation. The data collection contains information on all deaths processed by the National Center for Health Statistics for calendar year 1992. Each record in the file includes data on underlying cause and multiple cause of death. Data cover date of death, geographic location (region, state, county, division) of death, residence of the deceased (region, state, county, city, population size), and sex, race, age, marital status , state of birth, origin or descent, kind of business, and occupation of the deceased. The underlying causes of death are coded using The Manual of the International Statistical Classification Of Diseases, Injuries, and Cause-Of-Death, Ninth Revision (ICD-9).

Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science at the University of North Carolina in Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items may be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

"ASCII Version"

The National Death Index (NDI) is a centralized database of death record information on file in state vital statistics offices. Working with these state offices, the National Center for Health Statistics (NCHS) established the NDI as a resource to aid epidemiologists and other health and medical investigators with their mortality ascertainment activities. Assists investigators in determining whether persons in their studies have died and, if so, provide the names of the states in which those deaths occurred, the dates of death, and the corresponding death certificate numbers. Investigators can then make arrangements with the appropriate state offices to obtain copies of death certificates or specific statistical information such as manner of death or educational level. Cause of death codes may also be obtained using the NDI Plus service. Records from 1979 through 2011 are currently available and contain a standard set of identifying information on each death. Death records are added to the NDI file annually, approximately 12 months after the end of a particular calendar year. 2012 should be available summer 2014. Early Release Program for 2013 is now available. The NDI service is available to investigators solely for statistical purposes in medical and health research. The service is not accessible to organizations or the general public for legal, administrative, or genealogy purposes.

Purpose Little is known about differences in mortality among frequent and occasional participants of health check-ups. We aimed to compare mortality of frequent and occasional participants of the annual health examination in Taiwan. Subjects and methods We conducted a cohort study from 2001 until 2007. There were 25,166 participants aged 65 and over in the health check-ups in 2001. Of them, 19,768 participated in the subsequent check-ups (frequent participants), but 5398 participated only once (occasional participants). The outcome measures were all-cause, cancer and cardiovascular mortalities. Results Compared to frequent participants, multivariate-adjusted hazard ratio (MHR) for seven-year mortality was 3.28 [95% CI: 2.98–3.62] for occasional participants. In the propensity-score-matched subsample, MHR was 3.18 [95% CI: 2.78–3.65] for occasional participants. Stratified by their participation in increasing order of frequency, all-cause mortality rates per 1000 person-year were 48.89 [95% CI: 46.40–51.37], 30.24 [95% CI: 27.98–32.50], 23.36 [95% CI: 21.27–25.46], 14.88 [95% CI: 13.18–16.58], 8.58 [95% CI: 7.26–9.89], 3.23 [95% CI: 2.46–4.00], and 0.47 [95% CI: 0.19–0.75], respectively. Discussion The most likely cause of mortality reduction might be the beneficial effect of subsequent referrals after health check-ups. More frequent participation ensures necessary referrals and treatment were not missed. Screening for multiple diseases detects early cases of various diseases simultaneously. Periodic health examinations also lessen patient worry and improve delivery of preventive services.

Conclusion Occasional participants had higher mortalities as compared to frequent participants. This trend persisted after propensity matching. There was an inverse relationship between health examination participation and all-cause, cancer and cardiovascular mortalities.

Purpose and brief description The feto-infant mortality statistics are compiled on the basis of the declaration form of the death of a child under one year of age or of a stillborn child. Since 2010, the National Register has also been used to more accurately determine the relevant official life events and to check the main information. These statistics break down deaths into those before the age of one year old and infants who were stillborn, per gender, by administrative units of the country, by the main characteristics of the mother (age, civil status, state of union, level of education, professional status, nationality) and by certain characteristics of the delivery and of the newborns (location, way of giving birth, twin birth, weight, duration of the pregnancy, congenital defect). They also produce various indicators of feto-infant mortality and a breakdown of feto-infant deaths according to the age of death. Data collection method The feto-infant mortality statistics are compiled on the basis of two sources: the National Register of Natural Persons (NRPP) and the statistical declaration forms for a child under one year old or stillborn (Model IIID). These forms are an important source on infant mortality and provide a lot of information, especially health data. They also provide information about the circumstances of birth and about the parents of the deceased children. They are the only source of information on stillbirths or late fetal deaths. The information provided by the NR is less extensive, concerns only infant mortality, but is available more quickly; it contains the death of all children residing in Belgium (and therefore registered in the NR), regardless of whether the death took place in Belgium or abroad. Until 2009, these two sources were consolidated in relation to each other, but in the sense that the declaration forms served as a reference, with the NR being used mainly to provide the data that were missing or not requested on the declaration forms. Therefore, only the deaths (that took place in Belgium and were therefore) reported to the Belgian Registry Office were taken into account when compiling the infant mortality statistics, i.e. those for which the stated place of residence was a Belgian municipality. Since 2010, the statistics have been produced with the NR as reference. Henceforth, only the death of a child included in the NR will be taken into account. By using the NR, the death of a child abroad can be included in the statistics. It also makes it possible to acknowledge the death of children registered in the waiting register for refugees and asylum seekers. Population All feto-infant deaths Frequency Annually. Release calendar Results available 1 year after the reference period Definitions Deceased infant: death before the first birthday of a live-born child. Stillborn child: child who, at the time of birth, does not show any sign of life (such as breathing, heartbeat, pulsating of the umbilical cord, effective contraction of a muscle) and weighs at least 500 grams or, if the weight is unknown, had a gestational age of at least 22 weeks. Below this limit, we are talking about a premature fetal death that is not officially declared. Twin birth: Total number of births, including stillbirths, due to pregnancy Place of the child: Place of the child in the totality of living births to the mother Duration of the pregnancy: Duration of the pregnancy (in weeks) at the time of birth Way of giving birth: Type of assistance during birth Congenital defects: Presence of one or more congenital defects Weight: Weight (in grams) of the child at birth Apgar after 1 minute: Apgar score after 1 minute Apgar after 5 minutes: Apgar score after 5 minutes. Region: the child’s region of legal residence. In the case of a stillbirth: the mother’s region of habitual residence at the time of birth. Metadata Foeto-infantiele sterfte.pdf

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 tests, cases, and associated deaths that have been reported among Connecticut residents. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Hospitalization data were collected by the Connecticut Hospital Association and reflect the number of patients currently hospitalized with laboratory-confirmed COVID-19. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are reported daily, with timestamps indicated in the daily briefings posted at: portal.ct.gov/coronavirus. Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

Starting April 4, 2022, negative rapid antigen and rapid PCR test results for SARS-CoV-2 are no longer required to be reported to the Connecticut Department of Public Health as of April 4. Negative test results from laboratory based molecular (PCR/NAAT) results are still required to be reported as are all positive test results from both molecular (PCR/NAAT) and antigen tests.

On 5/16/2022, 8,622 historical cases were included in the data. The date range for these cases were from August 2021 – April 2022.”

Unbiased mortality estimates are fundamental for testing ecological and evolutionary theory as well as for developing effective conservation actions. However, mortality estimates are often confounded by dispersal, especially in studies where dead-recovery is not possible. In such instances, missing individuals (i.e. individuals with unobserved time of death) may have died or permanently emigrated from a study area, making inferences about their fate difficult. Mortality before and during dispersal, as well as the decision to disperse, usually depend on a suite of individual, social, and environmental covariates, which in turn can be used to draw conclusions about the fate of missing individuals.
Here, we propose a Bayesian hierarchical model that takes into account time-varying covariates to estimate transitions between life-history states and mortality in each state using mark-resighting data with missing individuals. Specifically, our framework estimates mortality rates in two states (resident and dispersing state) by treating the fate of missing individuals as a latent (i.e. unobserved) variable that is statistically inferred based on information from individuals with a known fate and given the individual, social, and environmental conditions at the time of disappearance. Our model also estimates rates of state transition (i.e. emigration) to assess whether a missing individual was more likely to have died or survived due to unobserved emigration from the study area.
We used simulations to check the validity of our model and assessed its performance with data of varying degrees of uncertainty. Our modeling framework provided accurate mortality and emigration estimates for simulated data of different sample sizes, proportions of missing individuals, and resighting intervals. Variation in sample size appeared to affect the precision of estimated parameters the most.
Our approach offers a solution to estimating unbiased mortality of both resident and dispersing individuals as well as the probability of emigration using mark-resighting data with incomplete death records. Conditional on the availability of data on known-fate individuals and relevant time-varying covariates, our model can reconstruct the fate (death or emigration) of missing individuals. The modularity of our framework allows mortality analyses to be tailored to a variety of species-specific life histories.

The notes field contains the full MEDLINE (Ovid) search strategy for observational studies on youth suicide mortality. The search file in this dataset contains the full MEDLINE (Ovid), EMBASE (Ovid), PsycInfo (Ovid) and CINAHL (EBSCO) strategies from March 7, 2025. The remaining files are the database downloads for each search; access to these files are restricted due to licensing agreements.

The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational Units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.

Outcomes of contingency tests among pairs of infections. Using the infectious diseases data collected from US death certificates, we used Chi-squared tests to find out whether each pair of infections were reported together more or less than expected from their occurrence alone.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by gender. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are reported daily, with timestamps indicated in the daily briefings posted at: portal.ct.gov/coronavirus. Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

BackgroundGlobally, with a neonatal mortality rate of 27/1000 live births, Sub-Saharan Africa has the highest rate in the world and is responsible for 43% of all infant fatalities. In the first week of life, almost three-fourths of neonatal deaths occur and about one million babies died on their first day of life. Previous studies lack conclusive evidence regarding the overall estimate of early neonatal mortality in Sub-Saharan Africa. Therefore, this review aimed to pool findings reported in the literature on magnitude of early neonatal mortality in Sub-Saharan Africa.MethodsThis review’s output is the aggregate of magnitude of early neonatal mortality in sub-Saharan Africa. Up until June 8, 2023, we performed a comprehensive search of the databases PubMed/Medline, PubMed Central, Hinary, Google, Cochrane Library, African Journals Online, Web of Science, and Google Scholar. The studies were evaluated using the JBI appraisal check list. STATA 17 was employed for the analysis. Measures of study heterogeneity and publication bias were conducted using the I2 test and the Eggers and Beggs tests, respectively. The Der Simonian and Laird random-effect model was used to calculate the combined magnitude of early neonatal mortality. Besides, subgroup analysis, sensitivity analysis, and meta regression were carried out to identify the source of heterogeneity.ResultsFourteen studies were included from a total of 311 articles identified by the search with a total of 278,173 participants. The pooled magnitude of early neonatal mortality in sub-Saharan Africa was 80.3 (95% CI 66 to 94.6) per 1000 livebirths. Ethiopia had the highest pooled estimate of early neonatal mortality rate, at 20.1%, and Cameroon had the lowest rate, at 0.5%. Among the included studies, both the Cochrane Q test statistic (χ2 = 6432.46, P