Reporting of Aggregate Case and Death Count data was discontinued 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.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

Dataset from LIFE-MEDEA participants (asthmatic children) from Cyprus and Greece, including Study ID, gender, age, study year, ambient temperature, ambient humidity, recording day, percentage of time staying at home, steps per day, callendar day, calendar week, date, lockdown status (phase 1, 2, or 3) due to COVID-19 pandemic, and if the date was during the weekend (binary variable).

Since the start of the COVID-19 pandemic in France, the level of concern towards the virus fluctuated a lot. While less than half of French people were not worried about the spread of the virus in January 2020, the share of worried people reached a peak (85 percent of respondents) during march 2020. Overall, there was a resurgence of concern about the COVID-19 virus with each announcement of a new lockdown by the French government.

The novel coronavirus (COVID-19)

Coronaviruses are a large family of viruses that cause illnesses ranging from a common cold to more severe conditions such as MERS or SARS. One of those viruses that have recently been identified is named COVID-19 or SARS-CoV-2. It was first detected in December 2019 in the Chinese city of Wuhan. According to the investigation of the Chinese authorities, people infected with the virus would have contracted it by consuming products (some of which are sold illegally) of animal origin from a large city market, the Huanan Seafood Wholesale Market (since closed). The transmission of this virus is likely to occur through the respiratory tract (exhalation, sneezing, coughing) and physical contact. The symptoms are similar to those of classic flu: fever, cough, breathing difficulties, muscle pain, and fatigue.

The pandemic situation in France

Although the data is changing daily, 5.7 million people infected by the COVID-19 virus have been identified in France since the beginning of the pandemic. Among them, more than 100,000 succumbed to this disease. As in many other countries, the French government is counting on its vaccination campaign to stop the epidemic.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

This data was generated as part of an 18 month ESRC funded project,as part of UKRI’s rapid response to COVID-19. The project examines how UK period poverty initiatives mitigated Covid-19 challenges in light of lockdown measures and closure of services, and how they continued to meet the needs of those experiencing period poverty across the UK. Applied social science research methodologies were utilised to collect and analyse data as this project, about the Covid-19 pandemic, was undertaken during an ongoing ‘real world’ pandemic. Data collection was divided into two phases. Phase 1 (October 2020 – February 2021) collected data from period poverty organisations in the UK using semi-structured interviews and an online survey to develop an in-depth understanding of how period poverty organisations were responding to and navigating the Covid-19 Pandemic. Having collected and analysed this data, phase 2 (June – September 2021) used an online survey to collect data from people experiencing period poverty in order to better understand their lived experiences during the pandemic. Our dataset comprises of phase 1 interview transcripts and online survey responses, and phase 2 online survey responses.

Period poverty refers not only to economic hardship with accessing period products, but also to a poverty of education, resources, rights and freedom from stigma for girls and menstruators (1). Since March 2020, and the introduction of lockdown/social distancing measures as a result of the Covid-19 pandemic, more than 1 of every 10 girls (aged 14-21) cannot afford period products and instead must use makeshift products (toilet roll, socks/other fabric, newspaper/paper). Nearly a quarter (22%) of those who can afford products struggle to access them, mostly because they cannot find them in the shops, or because their usual source/s is low on products/closed (2).

Community /non-profit initiatives face new challenges related to Covid-19 lockdown measures as they strive to continue to support those experiencing period poverty. Challenges include accessing stocks of period products, distribution of products given lockdown restrictions, availability of staff/volunteer assistance and the emergence of 'new' vulnerable groups. There is an urgent need to capture how initiatives are adapting to challenges, to continue to support the needs of those experiencing period poverty during the pandemic. This data is crucial to informing current practice, shaping policy, developing strategies within the ongoing crisis and any future crises, and ensuring women and girls' voices are centralised.

The project builds upon existing limited knowledge by providing insight into how UK based initiatives and projects are mitigating challenges linked to Covid-19, by examining how they are continuing to meet the needs of those experiencing period poverty and identifying any gaps in provision.

1. Montgomery P., et al., 2016. Menstruation and the Cycle of Poverty. PLoS ONE 11(12): e0166122.
2. Plan International UK, 2020. The State of Girls' Rights in the UK: Early insights into the impact of the coronavirus pandemic on girls. London: Plan International UK

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

• A “_coverage” append denotes how many times the facility reported that element during that collection week.
• A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.
• A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

For recent updates to the dataset, scroll to the bottom of the dataset description.

On May 3, 2021, the following fields have been added to this data set.

• hhs_ids
• previous_day_admission_adult_covid_confirmed_7_day_coverage
• previous_day_admission_pediatric_covid_confirmed_7_day_coverage
• previous_day_admission_adult_covid_suspected_7_day_coverage
• previous_day_admission_pediatric_covid_suspected_7_day_coverage
• previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum
• total_personnel_covid_vaccinated_doses_none_7_day_sum
• total_personnel_covid_vaccinated_doses_one_7_day_sum
• total_personnel_covid_vaccinated_doses_all_7_day_sum
• previous_week_patients_covid_vaccinated_doses_one_7_day_sum
• previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On September 20, 2021, the following has been updated: The use of analytic dataset as a source.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

On October 24, 2022, the data includes more analytical calculations in efforts to provide a cleaner dataset. For a raw version of this dataset, please follow this link: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

Frequency of the statement related with knowledge level on COVID-19 (KLC-19).

Abstract copyright UK Data Service and data collection copyright owner.

The Centre for Longitudinal Studies (CLS) and the MRC Unit for Lifelong Health and Ageing (LHA) have carried out two online surveys of the participants of five national longitudinal cohort studies which have collected insights into the lives of study participants including their physical and mental health and wellbeing, family and relationships, education, work, and finances during the coronavirus pandemic. The Wave 1 Survey was carried out at the height of lockdown restrictions in May 2020 and focussed mainly on how participants’ lives had changed from just before the outbreak of the pandemic in March 2020 until then. The Wave 2 survey was conducted in September/October 2020 and focussed on the period between the easing of restrictions in June through the summer into the autumn. A third wave of the survey was conducted in early 2021.

In addition, CLS study members who had participated in any of the three COVID-19 Surveys were invited to provide a finger-prick blood sample to be analysed for COVID-19 antibodies. Those who agreed were sent a blood sample collection kit and were asked to post back the sample to a laboratory for analysis. The antibody test results and initial short survey responses are included in a single dataset, the COVID-19 Antibody Testing in the National Child Development Study, 1970 British Cohort Study, Next Steps and Millennium Cohort Study, 2021 (SN 8823).

The CLS studies are:

• Millennium Cohort Study (born 2000-02) both cohort members and parents (MCS)
• Next Steps (born 1989-90) (NS)
• 1970 British Cohort Study (BCS70)
• 1958 National Child Development Study (NCDS).

The LHA study is:

• MRC National Survey of Health and Development, 1946 British birth cohort (NSHD)

The content of the MCS, NS, BCS70 and NCDS COVID-19 studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The COVID-19 Survey in Five National Longitudinal Cohort Studies: Millennium Cohort Study, Next Steps, 1970 British Cohort Study and 1958 National Child Development Study, 2020-2021 contains the data from waves 1, 2 and 3 for the 4 cohort studies. The data from all four CLS cohorts are included in the same dataset, one for each wave.

The COVID-19 Survey data for the 1946 birth cohort study (NSHD) run by the LHA is held under SN 8732 and available under Special Licence access conditions.

Latest edition information
For the fourth edition (June 2022), the following minor corrections have been made to the wave 3 data:

• corrections to a small number of cases where CW3_GROW and CW3_GROWB were incorrectly calculated
• recoded values and reformatted the code list for CW3_COVIDVAC as the original value of 3 was removed from the final version of the survey
  

The study covers physical and mental health topics, wellbeing, family and relationships, education, work, and finances.

Predictors of parental stress.

Effects of lockdown measures by destination city industrial composition (afternoon flows leaving the top 300 cities).

Demographic characteristics of the study sample.

Effects of lockdown measures by destination city industrial composition (excluding within-barangay flows).

Abstract

Following the outbreak of COVID-19 and lockdown measures introduced in Mauritius, Statistics Mauritius suspended all field activities involving face-to-face data collection. To monitor the socioeconomic effects of the pandemic on Mauritian households, Statistics Mauritius and the World Bank launched three rounds of a household telephone survey, the Rapid Continuous Multi-Purpose Household Surveys, from May to July 2020. The three survey rounds captured key information that was representative at the national level. The data were collected by a professional survey company using the computer-assisted telephone interviewing technology. The observation unit was the household head or a knowledgeable household member, except in the employment module (module 2), wherein household members were asked to respond individually to the extent they were available to do so at the time of the interview; response by proxy was otherwise accepted.

Geographic coverage

National coverage

Analysis unit

Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

A Stratified two-stage sampling design is used. At the first stage, Primary Sampling Units (PSUs) are selected in proportion to square root of the total number of households in the geographical district and at the second stage a fixed number of households is selected from each selected PSU. The Relative Development Index (RDI) is used as the spatial stratification factor. This index is based on 12 variables encompassing housing and living conditions, literacy and education, and employment derived from the 2011 Housing and Population Census to rank PSUs. A set of RDIs for administrative regions has been published in the series "Economic and Social Indicators" - Issue No. 977. The second stage stratification criteria are community, household size and average monthly expenditure of the household.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire comprises seven modules. - Module 1 covers the general characteristics of the population. - Module 2 solicits information about economic activities on every household member ages 16 to 64 not in full time education. - Module 3 covers access to basic food and services including health and education. - Module 4 captures information on food security. - Module 5 covers information regarding changes in household income. - Module 6 captures information regarding the type of strategies adopted by households to cope with shocks, and module 7 covers safety nets since the lockdown.

Background: The novel coronavirus 2019 (COVID-19) has caused severe panic among people worldwide. In Italy, a nationwide state of alert was declared on January 31st, leading to the confinement of the entire population from March 11 to May 18, 2020. Isolation and quarantine measures cause psychological problems, especially for individuals who are recognized as being vulnerable. Parental bonding and attachment styles play a role in the programming of the stress response system. Here, we hypothesize that the response to restricted social contact and mobility due to the pandemic has detrimental effects on mental-psychological health and that this relationship is, at least in part, modulated by parental bonding and attachment relationships that are experienced at an early age.Methods: A sample of 68 volunteer University students was screened for psychopathological symptoms (SCL-90-R and STAI-Y), stress perception (PSS), attachment style (RQ), and parental care and overcontrol (PBI) 6 months before the confinement. In the same subjects, psychopathological symptoms and stress perception were measured again during confinement.Results: Overall, psychological health and stress management deteriorated across the entire sample during confinement. Specifically, a significant increase in phobic anxiety, depression, psychological distress, and perceived stress was observed. Notably, parental bonding and attachment styles modulated the psychological status during the lockdown. Individuals with secure attachment and high levels of parental care (high care) showed increased levels of state anxiety and perceived stress in phase 2, compared with phase 1. In contrast, individuals with insecure attachment and low levels of parental care (low care) already showed a high rate of state anxiety and perceived stress in phase 1 that did not increase further during phase 2.Conclusion: The general deterioration of psychological health in the entire sample demonstrates the pervasiveness of this stressor, a decline that is partially modulated by attachment style and parental bonding. These results implicated disparate sensitivities to environmental changes in the high- and low care groups during the lockdown, the former of which shows the greatest flexibility in the response to environment, suggesting adequate and functional response to stress in high care individuals, which is not observable in the low care group.

Objective: To evaluate if the number of admitted extremely preterm (EP) infants (born before 28 weeks of gestational age) differed in the neonatal intensive care units (NICUs) of the SafeBoosC-III consortium during the global lockdown when compared to the corresponding time period in 2019.Design: This is a retrospective, observational study. Forty-six out of 79 NICUs (58%) from 17 countries participated. Principal investigators were asked to report the following information: (1) Total number of EP infant admissions to their NICU in the 3 months where the lockdown restrictions were most rigorous during the first phase of the COVID-19 pandemic, (2) Similar EP infant admissions in the corresponding 3 months of 2019, (3) the level of local restrictions during the lockdown period, and (4) the local impact of the COVID-19 lockdown on the everyday life of a pregnant woman.Results: The number of EP infant admissions during the first wave of the COVID-19 pandemic was 428 compared to 457 in the corresponding 3 months in 2019 (−6.6%, 95% CI −18.2 to +7.1%, p = 0.33). There were no statistically significant differences within individual geographic regions and no significant association between the level of lockdown restrictions and difference in the number of EP infant admissions. A post-hoc analysis based on data from the 46 NICUs found a decrease of 10.3%in the total number of NICU admissions (n = 7,499 in 2020 vs. n = 8,362 in 2019).Conclusion: This ad hoc study did not confirm previous reports of a major reduction in the number of extremely pretermbirths during the first phase of the COVID-19 pandemic.Clinical Trial Registration:ClinicalTrial.gov, identifier: NCT04527601 (registered August 26, 2020), https://clinicaltrials.gov/ct2/show/NCT04527601.

Effects of lockdown measures by destination city firm size composition (baseline).

Effects of lockdown measures by destination city industrial composition (baseline).

Background: The COVID-19 pandemic has led to an increase in the risk of suicide, uncertainty, mental stress, terror, annoyance, weariness, financial issues, and frustration. We aim to determine the prevalence of insomnia, depressive and anxiety symptoms, and their associated factors among Libyan populations during the COVID-19 pandemic and the civil war.Methods: An online cross-sectional survey was conducted among the Libyan population between July 18 and August 23, 2020. The data collected included basic demographic characteristics, level of education, employment status, COVID-19-related questions, and questions about abuse and domestic violence. This study assessed the psychological status of participants who were screened for anxiety symptoms using the seven-item Generalized Anxiety Disorder scale (GAD-7). Depressive symptoms were also screened for using the two-item Patient Health Questionnaire (PHQ-2) and the Insomnia Severity Index (ISI). Binomial logistic regression was used to predict the probability of insomnia, anxiety and depressive symptoms.Results: A total of 10,296 responses were recorded. Among the participants, 4,756 (46.2%) obtained a cut-off score of ≥ 3 which indicated depressive symptoms. For anxiety, 1,952 participants (19%) obtained a cut-off score of ≥ 15, which indicated anxiety symptoms. For the ISI, the mean (SD) was 11.4 (6.1) for the following categories: no clinical insomnia (0–7) 3,132 (30.4%), sub-threshold insomnia (1–7) 3,747 (36.4%), moderate severity clinical insomnia (8–14) 2,929 (28.4%), and severe clinical insomnia (15–21) 488 (4.7%). Logistic regression analysis showed that depressive symptoms were statistically associated with age, marital status, education level, occupational category, financial problems during the COVID-19 pandemic, health status, having a COVID-19 infection, current health status, suicide ideation, abuse or domestic violence, and lockdown compliance (p < 0.05). The regression analysis revealed a statistically significant association between anxiety symptoms and age, education level, occupational status, financial problems during the COVID-19 pandemic, having a COVID-19 infection, health status, suicide ideation, abuse or domestic violence, and lockdown compliance (p < 0.05). The regression analysis revealed a statistically significant association between insomnia and all study variables with the exception of age, educational level, and occupational status (p < 0.05).Conclusion: Confronted with the COVID-19 outbreak, the Libyan population exhibited high levels of psychological stress manifested in the form of depressive and anxiety symptoms, while one-third of the Libyan population suffered from clinical insomnia. Policymakers need to promote effective measures to reduce mental health issues and improve people's quality of life during the civil war and the COVID-19 pandemic.

The novelty of the coronavirus disease 2019 (COVID-19) pandemic is that it is occurring in a globalized society enhanced by digital capabilities. Our aim was to analyze the psychological and emotional states of participants in different pandemic-related contexts, with a focus on their digital and physical distancing behaviors. The online survey was applied during the ascending phase of the pandemic in March 2020 in two neighboring EU countries: Italy and Croatia. The study subjects involved four groups, two directly affected by epidemiological measures and two serving as controls—(1) participants from Italy who were in lockdown (Italy group), (2) participants from Croatia who were not in lockdown but who were in direct contact with an infected person and underwent epidemiological measures (CRO-contact group), (3) participants from Croatia who were in an analogous situation but not near the same infected person (CRO-no contact group), and (4) participants from Croatia who were not aware of any infected person (CRO-unrelated group). The survey consisted of validated scales of psychological and emotional states, and custom-made questionnaires on the digital (online) and physical (off-line) behavior of the participants. The Italy group in lockdown had higher self-perceived scores for depression, stress, post-traumatic intrusion, and avoidance, as well as the highest digital activity and physical distancing than the not-in-lockdown Croatian groups. The insight into the extent of online activities and off-line isolation allowed for the introduction of Digital Activity and Physical Distancing Scores. Self-perceived post-traumatic avoidance was higher in both the Italy and CRO-contact groups than the control CRO-no contact and CRO-unrelated groups, and higher avoidance correlated with higher Digital Activity and Physical Distancing Scores. Being in direct contact with the infected person, the CRO-contact group had no other alterations than unexpectedly lower post-traumatic hyperarousal when compared with the Italy group. The Italy group in lockdown demonstrated higher self-perceived psychological toll together with higher digital activity and physical distancing than Croatian groups not in lockdown, even when compared with the affected CRO-contact group. The study outcomes suggest that the general emergency measures influenced citizens in lockdown more than exposure to the virus through direct contact with an infected person.

BackgroundPrevious investigations suggest that the COVID-19 pandemic effects on alcohol consumption were heterogenous and may vary as a function of structural and psychological factors. Research examining mediating or moderating factors implicated in pandemic-occasioned changes in drinking have also tended to use single-study cross-sectional designs and convenience samples. Aims: First, to explore structural (changed employment or unemployment) and psychological (subjective mental health and drinking motives) correlates of consumption reported during the COVID-19 pandemic using a UK nationally representative (quota sampled) dataset. Second, to determine whether population-level differences in drinking during the COVID-19 pandemic (versus pre-pandemic levels) could be attributable to drinking motives. Method: Data collected from samples of UK adults before and during the pandemic were obtained and analysed: Step1 carried out structural equation modelling (SEM) to explore data gathered during a period of social restrictions after the UK’s first COVID-19-related lockdown (27 August-15 September, 2020; n = 3,798). It assessed whether drinking motives (enhancement, social, conformity, coping), employment and the perceived impact of the pandemic on subjective mental health may explain between-person differences in self-reported alcohol consumption. Step 2 multigroup SEM evaluated data gathered pre-pandemic (2018; n = 7,902) in concert with the pandemic data from step 1, to test the theory that population-level differences in alcohol consumption are attributable to variances in drinking motives. Results: Analyses of the 2020 dataset detected both direct and indirect effects of subjective mental health, drinking motives, and employment matters (e.g., having been furloughed) on alcohol use. Findings from a multigroup SEM were consistent with the theory that drinking motives explain not only individual differences in alcohol use at both time points, but also population-level increases in use during the pandemic. Conclusion: This work highlights socioeconomic and employment considerations when seeking to understand COVID-19-related drinking. It also indicates that drinking motives may be particularly important in explaining the apparent trend of heightened drinking during the pandemic. Limitations related to causal inference are discussed.