Unplanned public K-12 school district and individual school closures due to COVID-19 in the United States from August 1, 2020–June 30, 2022.

Every day, schools, child care centres and licensed home child care agencies report to the Ministry of Education on children, students and staff that have positive cases of COVID-19. If there is a discrepancy between numbers reported here and those reported publicly by a Public Health Unit, please consider the number reported by the Public Health Unit to be the most up-to-date. Schools and school boards report when a school is closed to the Ministry of Education. Data is current as of 2:00 pm the previous day. This dataset is subject to change. Data is only updated on weekdays excluding provincial holidays Effective June 15, 2022, board and school staff will not be expected to report student/staff absences and closures in the Absence Reporting Tool. The ministry will no longer report absence rates or school/child care closures on Ontario.ca for the remainder of the school year. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. ##Summary of school closures This is a summary of school closures in Ontario. Data includes: * Number of schools closed * Total number of schools * Percentage of schools closed ##School Absenteeism This report provides a summary of schools and school boards that have reported staff and student absences. Data includes: * School board * School * City or Town * Percentage of staff and students who are absent ##Summary of cases in schools This report provides a summary of COVID-19 activity in publicly-funded Ontario schools. Data includes: * School-related cases (total) * School-related student cases * School-related staff cases * Current number of schools with a reported case * Current number of schools closed Note: In some instances the type of cases are not identified due to privacy considerations. ##Schools with active COVID-19 cases This report lists schools and school boards that have active cases of COVID-19. Data includes : * School Board * School * Municipality * Confirmed Student Cases * Confirmed Staff Cases * Total Confirmed Cases ##Cases in school board partners This report lists confirmed active cases of COVID-19 for other school board partners (e.g. bus drivers, authorized health professionals etc.) and will group boards if there is a case that overlaps. Data includes : * School Board(s) * School Municipality * Confirmed cases – other school board partners ##Summary of targeted testing conducted in schools This data includes all tests that have been reported to the Ministry of Education since February 1, 2021. School boards and other testing partners will report to the Ministry every Wednesday based on data from the previous seven days. Data includes : * School boards or regions * Number of schools invited to participate in the last seven days * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified ##Summary of asymptomatic testing at conducted in pharmacies: This is a summary of COVID-19 rapid antigen testing conducted at participating pharmacies in Ontario since March 27, 2021. * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified

This dataset tracks the updates made on the dataset "COVID-19-related School Closures: USA, 2020-2022" as a repository for previous versions of the data and metadata.

COVID-19-associated school closures, United States, February 18–June 30, 2020

  Description

Unplanned public K-12 school district and individual school closures due to COVID-19 in the United States from February 18–June 30, 2020.

  Dataset Details

Publisher: Centers for Disease Control and Prevention Last Modified: 2022-01-12 Contact: Nicole Zviedrite (jmu6@cdc.gov)

  Source

Original data can be found at: https://data.cdc.gov/d/wgvr-7mvz

  Usage… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/covid-19-associated-school-closures-united-states.

The 2021-2022 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2021-2022 school year and the Fall 2022 semester, from August 2021 – December 2022.

These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the https://nces.ed.gov/ccd/files.asp#Fiscal:2,LevelId:5,SchoolYearId:35,Page:1">National Center for Educational Statistics (NCES) for 2020-2021.

School learning modality types are defined as follows:

• In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels.
• Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels.
• Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students.

• School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21].
• You can read more about the model in the CDC MMWR: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e2.htm" target="_blank">COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021.
• The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes:
  • Public school district that is NOT a component of a supervisory union
  • Public school district that is a component of a supervisory union
  • Independent charter district
• “BI” in the state column refers to school districts funded by the Bureau of Indian Education.

• Data from August 1, 2021 to June 24, 2022 correspond to the 2021-2022 school year. During this time frame, data from the AEI/Return to Learn Tracker and most state dashboards were not available. Inferred modalities with a probability below 0.6 were deemed inconclusive and were omitted. During the Fall 2022 semester, modalities for districts with a school closure reported by Burbio were updated to either “Remote”, if the closure spanned the entire week, or “Hybrid”, if the closure spanned 1-4 days of the week.
• Data from August 1, 2022 to December 31, 2022 correspond to the 2022-2023 school year and were processed in a similar manner to data from the 2021-2022 school year.
• Data for the month of July may show “In Person” status although most school districts are effectively closed during this time for summer break. Users may wish to exclude July data from use for this reason where applicable.

1. K-12 School Opening Tracker. Burbio 2021; https://cai.burbio.com/school-opening-tracker/. June 2021.
2. COVID-19 IMPACT: School District Operational Status. MCH Strategic Data: 2021; https://www.mchdata.com/covid19/schoolclosings. (30 June 2021, date last accessed).
3. Return to Learn Tracker. American Enterprise Institute 2021; https://www.returntolearntracker.net/. (30 June 2021, date last accessed).
4. Colorado School District Reopening Plans. Colorado Department of Education 2021; https://docs.google.com/spreadsheets/d/e/2PACX-1vQipdjO8QWhilhhJ4bX0FBebnHEzK1G3LEDQbE_S-xRvs2t0oHNm--acHwMRFmL9uKw4cXcOUqy1V66/pubhtml. (30 June 2021, date last accessed).
5. Connecticut Report Cards. Connecticut State Department of Education 2021; http://edsight.ct.gov/SASPortal/main.do. (30 June 2021, date last accessed).
6. COVID Planning Tool. Hawaii Department of Education 2021; https://public.tableau.com/views/COVIDPlanningTool-Embed700px/SimpleDashboard. (30 June 2021, date last accessed).
7. Idaho School District Status. Idaho State Department of Education 2021; https://ioem.maps.arcgis.com/apps/opsdashboard/index.html#/379299e56f234fa89cff3c8c6acf3ac8. (30 June 2021, date last accessed).
8. How Illinois Districts Are Providing Instruction: Virtual, In-Person, and Blended Learning. Illinois State Board of Education 2021; https://isp.maps.arcgis.com/apps/opsdashboard/index.html#/fceeacb37da04de4b237ed941dd7d5c4. (30 June 2021, date last accessed).
9. Digital Learning Model Schools. Louisiana Department of Education 2021; https://analytics.la.gov/t/DOE/views/DigitalLearningModel/SchoolLevelModeofLearning. (30 June 2021, date last accessed).
10. Safe Learning Model Dashboard. Minnesota Department of Education 2021; https://analytics.education.state.mn.us/t/MDEPublic/views/MN_Safe_Learning_Model/Dashboard. (30 June 2021, date last accessed).
11. Coronavirus (COVID-19) information. Missouri Department of Elementary & Secondary Education 2021; https://dese.mo.gov/communications/coronavirus-covid-19-information. (30 June 2021, date last accessed).
12. North Carolina School Dashboard. North Carolina Department of Public Instruction 2021. https://docs.google.com/spreadsheets/d/1We8gDpa4Do5NR83Nf8niGE_YxzLDf-KZh-tVWifStxE/edit#gid=0. (30 June 2021, date last accessed).
13. Ring the Bell: Return to In-Person Learning. New Mexico Public Education Department 2021; https://webnew.ped.state.nm.us/reentry-district-and-school-guidance/. (30 June 2021, date last accessed).
14. Reset and Restart Education. Ohio Department of Education 2021; http://education.ohio.gov/Topics/Reset-and-Restart. (30 June 2021, date last accessed).
15. 2020-21 School Status. Oregon Department of Education 2021; https://www.oregon.gov/ode/students-and-family/healthsafety/Pages/2020-21-School-Status.aspx. (30 June 2021, date last accessed).
16. Current Operational Status. South Carolina Department of Education 2021; https://ed.sc.gov/districts-schools/schools/district-and-school-closures/operational-status/#blk2102. (30 June 2021, date last accessed).
17. Tennessee Department of Education COVID-19 District Information. Tennessee Department of Education 2021; https://districtinformation.tnedu.gov/covid-information/search/-1. (30 June 2021, date last accessed).
18. State Snapshot: Virginia School Operational Status. Virginia Department of Education 2021; https://www.doe.virginia.gov/support/health_medical/office/reopen-status.shtml. (30 June 2021, date last accessed).
19. Current Reopening Status of Vermont's K-12 Public Schools. Vermont Department of Financial Regulation 2021; https://dfr.vermont.gov/about-us/covid-19/school-reopening#public-school. (30 June 2021, date last accessed).
20. School Reopening Data. Washington Office of Superintendent of Public Instruction 2021; https://www.k12.wa.us/about-ospi/press-releases/novel-coronavirus-covid-19-guidance-resources/school-reopening-data. (30 June 2021, date last accessed).
21. Public school districts. National Center for Education Statistics 2021; https://nces.ed.gov/ccd/districtsearch/. (30 June 2021, date last accessed).

Abstract

COVID-19 caused significant disruption to the global education system. A thorough analysis of recorded learning loss evidence documented since the beginning of the school closures between March 2020 and March 2022 finds even evidence of learning loss. Most studies observed increases in inequality where certain demographics of students experienced more significant learning losses than others. But there are also outliers, countries that managed to limit the amount of loss. This review consolidates all the available evidence and documents the empirical findings. Data for 41 countries is included, together with other variables related to the pandemic experience. This data is publicly available and will be updated regularly.

Geographic coverage

The data covers 41 countries.

Analysis unit

Country

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

The COVID-19 pandemic had severe impacts on almost every aspect of life, from health via economy to education. School closures around the world caused disruptions in learning development of children and youth. South Asia as well as Latin America and the Caribbean had the highest number of weeks where schools were either partially or fully closed. In the former, a total of 84 weeks of education were conducted either partially or completely remote. On the other hand, Europe and Central Asia saw just above 30 weeks of some form of remote learning. Infrastructure and remote learning It may not come as a surprise, then, that South Asia and Latin America and the Caribbean were the two regions with the highest levels of learning delays caused by the COVID-19 pandemic. Moreover, different countries in different regions have different infrastructures that make remote learning possible. For instance, Sub-Saharan Africa, where many countries have a poor internet infrastructure, was the region with the highest number of academic weeks held in person as remote learning was impossible in many areas. Economic impact
The learning disruptions caused by the pandemic could also have severe economic impacts in the future if counter measures are not taken. Estimates show that globally, *** trillion U.S. dollars of GDP could be lost annually by 2040 due to the educational disruptions caused by COVID-19.

We conducted a phone-based survey from December 2021 to February 2022, collecting responses for 4,750 Pakistani households with school-aged children, in all four provinces. Respondents were selected by random digit dialling and a short screening call to assess eligibility and determine region, allowing subsequent stratification by province and zone.

Every day, schools, child care centres and licensed home child care agencies report to the Ministry of Education on children, students and staff that have positive cases of COVID-19. If there is a discrepancy between numbers reported here and those reported publicly by a Public Health Unit, please consider the number reported by the Public Health Unit to be the most up-to-date. Schools and school boards report when a school is closed to the Ministry of Education. Data is current as of 2:00 pm the previous day. This dataset is subject to change. Data is only updated on weekdays excluding provincial holidays Effective June 15, 2022, board and school staff will not be expected to report student/staff absences and closures in the Absence Reporting Tool. The ministry will no longer report absence rates or school/child care closures on Ontario.ca for the remainder of the school year. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. ##Summary of school closures This is a summary of school closures in Ontario. Data includes: * Number of schools closed * Total number of schools * Percentage of schools closed ##School Absenteeism This report provides a summary of schools and school boards that have reported staff and student absences. Data includes: * School board * School * City or Town * Percentage of staff and students who are absent ##Summary of cases in schools This report provides a summary of COVID-19 activity in publicly-funded Ontario schools. Data includes: * School-related cases (total) * School-related student cases * School-related staff cases * Current number of schools with a reported case * Current number of schools closed Note: In some instances the type of cases are not identified due to privacy considerations. ##Schools with active COVID-19 cases This report lists schools and school boards that have active cases of COVID-19. Data includes : * School Board * School * Municipality * Confirmed Student Cases * Confirmed Staff Cases * Total Confirmed Cases ##Cases in school board partners This report lists confirmed active cases of COVID-19 for other school board partners (e.g. bus drivers, authorized health professionals etc.) and will group boards if there is a case that overlaps. Data includes : * School Board(s) * School Municipality * Confirmed cases – other school board partners ##Summary of targeted testing conducted in schools This data includes all tests that have been reported to the Ministry of Education since February 1, 2021. School boards and other testing partners will report to the Ministry every Wednesday based on data from the previous seven days. Data includes : * School boards or regions * Number of schools invited to participate in the last seven days * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified ##Summary of asymptomatic testing at conducted in pharmacies: This is a summary of COVID-19 rapid antigen testing conducted at participating pharmacies in Ontario since March 27, 2021. * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified

The COVID-19 outbreak led to widespread school closures and the shift to remote teaching, potentially resulting in lasting negative impacts on teachers’ psychological well-being due to increased workloads and a perceived lack of administrative support. Despite the significance of these challenges, few studies have delved into the long-term effects of perceived instructional leadership on teachers’ psychological health. To bridge this research gap, we utilized longitudinal data from 927 primary and secondary school teachers surveyed in two phases: Time 1 in mid-November 2021 and Time 2 in early January 2022. Using hierarchical linear modeling (HLM), our findings revealed that perceptions of instructional leadership, especially the "perceived school neglect of teaching autonomy" at Time 1 were positively correlated with burnout levels at Time 2. Additionally, burnout at Time 2 was positively associated with psychological distress and acted as a mediator between the "perceived school neglect of teaching autonomy" and psychological distress. In light of these findings, we recommend that schools prioritize teachers’ teaching autonomy and take proactive measures to mitigate burnout and psychological distress, aiming for the sustainable well-being of both teachers and students in the post-pandemic era.

The COVID-19 U.S. State Policy Database tracks state policies in response to the COVID-19 pandemic. The study was created by researchers at the Boston University School of Public Health and includes data on closures, shelter-in-place orders, housing protections, changes to Medicaid and SNAP, physical distancing closures, reopening, and more. Policies included are state-wide directives or mandates, not guidance or recommendations. In order for a policy to be included, it must have applied to the entire state.

The COVID-19 outbreak led to widespread school closures and the shift to remote teaching, potentially resulting in lasting negative impacts on teachers’ psychological well-being due to increased workloads and a perceived lack of administrative support. Despite the significance of these challenges, few studies have delved into the long-term effects of perceived instructional leadership on teachers’ psychological health. To bridge this research gap, we utilized longitudinal data from 927 primary and secondary school teachers surveyed in two phases: Time 1 in mid-November 2021 and Time 2 in early January 2022. Using hierarchical linear modeling (HLM), our findings revealed that perceptions of instructional leadership, especially the "perceived school neglect of teaching autonomy" at Time 1 were positively correlated with burnout levels at Time 2. Additionally, burnout at Time 2 was positively associated with psychological distress and acted as a mediator between the "perceived school neglect of teaching autonomy" and psychological distress. In light of these findings, we recommend that schools prioritize teachers’ teaching autonomy and take proactive measures to mitigate burnout and psychological distress, aiming for the sustainable well-being of both teachers and students in the post-pandemic era.

The COVID-19 pandemic had severe impacts on almost every aspect of life, from health via economy to education. School closures around the world caused disruptions in learning development of children and youth. Estimates from 2022 show that South Asia was the region hardest affected by the pandemic, with a learning delay of over *** year. Europe and Central Asia, on the other hand, had the lowest delay with less than four months behind usual progress levels.

This dataset accompanies the PhD thesis Pupil Competence During the COVID-19-induced School Closures: An Analysis of the Effect of Distance Learning and Remediation Policies Using International Assessment Data in 30 Countries. The dataset compiles country-level data derived from large-scale international student assessments, specifically PISA and PIRLS, covering the period 2000–2022. It was created by harmonising publicly available microdata from the OECD (for PISA) and IEA (for PIRLS), aggregated to the national level. The data were collected and processed using StataNow 18.5. The dataset can be opened in StataNow 18.5 software. Stata .do files are also provided to allow full reproducibility of the data preparation and analysis. The dataset is specifically structured to support advanced statistical modelling, including Latent Growth Curve Modelling (LGCM), Synthetic Control (SC), and Synthetic Difference-in-Differences (SDID), to examine the effects of COVID-19 policies on pupil competence across diverse national contexts. Date Request Form: https://library.soton.ac.uk/datarequest

The COVID-19 pandemic had severe impacts on almost every aspect of life, from health via economy to education. School closures around the world caused disruptions in learning development of children and youth. Estimates from 2022 show that globally, the annual gross domestic product (GDP) loss could amount to nearly ***** billion U.S. dollars annually if no counter measures are taken. The economic damage was predicted to be highest in East Asia and the Pacific, and the lowest in Sub-Saharan Africa.

The COVID-19 outbreak led to widespread school closures and the shift to remote teaching, potentially resulting in lasting negative impacts on teachers’ psychological well-being due to increased workloads and a perceived lack of administrative support. Despite the significance of these challenges, few studies have delved into the long-term effects of perceived instructional leadership on teachers’ psychological health. To bridge this research gap, we utilized longitudinal data from 927 primary and secondary school teachers surveyed in two phases: Time 1 in mid-November 2021 and Time 2 in early January 2022. Using hierarchical linear modeling (HLM), our findings revealed that perceptions of instructional leadership, especially the "perceived school neglect of teaching autonomy" at Time 1 were positively correlated with burnout levels at Time 2. Additionally, burnout at Time 2 was positively associated with psychological distress and acted as a mediator between the "perceived school neglect of teaching autonomy" and psychological distress. In light of these findings, we recommend that schools prioritize teachers’ teaching autonomy and take proactive measures to mitigate burnout and psychological distress, aiming for the sustainable well-being of both teachers and students in the post-pandemic era.

Abstract

Like the rest of the world, Sudan has been experiencing the unprecedented social and economic impact of the COVID-19 pandemic. From restrictions on movement to school closures and lockdowns, the economic situation worsened, and commodity prices soared across the country. Results from the first six rounds of the High-Frequency Phone survey indicated that household welfare was negatively affected. The situation led to the loss of employment and income, decreased access to essential commodities and services, and food insecurity, particularly among the poor and vulnerable Sudanese. Moreover, the inability to access food and medicine degraded in July/August 2021 despite a slight amelioration in February/April 2021.

After COVID-19 in 2020, Sudan experienced situations that are more likely to compromise the recovery process. Political instability, unrest, and protests occurred before and after the military takeover in October 2021. Meanwhile, Sudan Central Bank devalued the currency, which may increase the already high commodities price. Besides, Sudan encountered historic flooding since the onset of the rainy season between May and June 2022. To monitor and assess the dynamics of the impacts of the country's economic and political situation (high inflation, social unrest, food shortages, asset loss, displacement, etc.) on households' welfare, another round of the Sudan High-Frequency Phone survey took place in June to August 2022.

Similar to the six previous rounds, the survey was conducted using mobile phones and covered all 18 states of Sudan. Round 7 sample is composed of 2816 Households from both urban and rural areas of Sudan. This sample allows us to draw statistical inferences about the Sudanese population at the national and rural/urban levels. The risk of nonresponse was a concern, so efforts were made to minimize this risk, including follow-up with respondents who failed to respond and keep the interviews short (15–20 minutes) to reduce respondent fatigue.

The questions are similar to the previous six rounds of the High-Frequency Phone survey but with added context. Households are asked about the key channels through which individuals and households are expected to be affected by the exchange rate distortions, country political instability, or flooding that occurred in May/June 2022, as well as how they have recovered from the COVID-19 pandemic impacts. Furthermore, questions cover a range of topics/themes including, but not limited to, health conditions, access to health facilities, access to other social services, availability of common food and non-food items (including medicines), nutrition and food security, employment/labor, income, assets, coping strategies, remittances, subjective welfare, climate/weather events, and the safety nets assistance.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Sampling procedure

The sampling methodology adopted for the implementation of this survey is probabilistic. Each of the units in the targeted population of the study must have a nonzero and known probability of selection. The sample was stratified by rural/urban for all 18 states. The distribution of the sub-sample between states and rural/urban is proportional to the size of the individuals owning mobile phones, i.e., not equal allocation. The selection of the individual phones (the households) is random, i.e., with equal probability, using a systematic sample procedure in the list (frame) of phones. This allows for extrapolating the results of the sample to the target population and estimating the precision of the results obtained. However, the implementation of this approach requires the availability of an adequate sampling frame containing all the units of the population without omissions or duplications.

In this survey, the sampling frame is provided by the phone lists. Considerable efforts were made to compile the frame using multiple lists of phone numbers collected during the implementation of various projects/surveys during the last few years at the household level across the country. This reduces the chances of having more than one phone number per household. Moreover, the interviewers double-checked during data collection that only one number was called for each selected surveyed household. Therefore, selecting individual phone numbers is the same as selecting households. It is worth noting that for West Kordofan and Central Darfur, the proportionality of rural/urban cannot be done according to the size of phones since there are no details for rural/urban. So, the size of the rural and urban populations (projection 2020) was used instead.

In Sudan, under the present federal system, the state is considered a semiautonomous entity mandated to take care of the affairs of the citizen, provide governance, and be responsible for planning, policy formulation, and implementation of the annual program. Consequently, the sample needed to cover all 18 states of the country. The sample is conceived to provide reliable estimates for the country (urban and rural) and to give statistically meaningful results at the national level.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

BASELINE (ROUND 1): One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Knowledge regarding the spread of COVID-19 - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, financial services) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets

ROUND 2: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Knowledge regarding the spread of COVID-19 - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, financial services, water, transportation, housing, internet, energy) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets ROUND 3: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, financial services) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets ROUND 4: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Youth module screening - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, transportation, fuel) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets ROUND 5: One questionnaire, the Household Questionnaire, was administered to all households in the sample. Respondent were asked to think about each child in their household for the education question. The Household Questionnaire provides information on: - Demographics - Mental health of the respondent - Children education.

ROUND 6: One questionnaire, the Household Questionnaire, was administered to all households in the sample. One youth per household is interviewed in the youth section of the questionnaire. The Questionnaire provides information on: - Demographics - Access to basic goods (medicines, staple food) - Youth employment - Youth job search - Youth aspirations and expectations - Youth skills and mental health.

ROUND 7: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Geography - Access to basic goods and services (medicines, staple food, health, education, water, housing, electricity) - Employment - Income loss - Food insecurity experience - Welfare - Experience of Climate/Weather events - Shocks and Coping strategies

Response rate

BASELINE (ROUND 1): A total of 4,032 households were successfully interviewed during the first round of data collection (conducted during June 16–July 5, 2020). Selected households from each state include both rural and urban households, with the representation of each state in the final sample being proportional to the state’s population relative to the overall population. Households who refused to tell their location (mode of living and state) were dropped to minimize bias. The final sample size accounts 4,027 households.

ROUND 2: Interviewers attempted to contact and interview all 4,032 households that were successfully interviewed in the baseline of the Sudan HFS on COVID-19. 2,989 households were successfully interviewed in the second round. However, households who refused to tell their location (mode of living and state) were dropped to minimize bias. The final sample size accounts 2,987 households.

ROUND 3: Interviewers attempted to contact and interview all 4,032 households that were successfully interviewed in the Baseline of the Sudan HFS on COVID-19. 2,990 households were successfully interviewed in the third round. Households who refused to tell their location (mode of living and state) were dropped to minimize bias. The final sample size accounts 2,987 households.

ROUND 4: Interviewers attempted to contact and interview all 4,032 households that were successfully interviewed in the Baseline of the Sudan

BackgroundThe use of Non-Pharmaceutical Interventions (NPIs), such as lockdowns, social distancing and school closures, against the COVID-19 epidemic is debated, particularly for the possible negative effects on vulnerable populations, including children and adolescents. This study therefore aimed to quantify the impact of NPIs on the trend of pediatric hospitalizations during 2 years of pandemic compared to the previous 3 years, also considering two pandemic phases according to the type of adopted NPIs.MethodsThis is a multicenter, quasi-experimental before-after study conducted in 12 hospitals of the Emilia-Romagna Region, Northern Italy, with NPI implementation as the intervention event. The 3 years preceding the beginning of NPI implementation (in March 2020) constituted the pre-pandemic phase. The subsequent 2 years were further subdivided into a school closure phase (up to September 2020) and a subsequent mitigation measures phase with less stringent restrictions. School closure was chosen as delimitation as it particularly concerns young people. Interrupted Time Series (ITS) regression analysis was applied to calculate Hospitalization Rate Ratios (HRR) on the diagnostic categories exhibiting the greatest variation. ITS allows the estimation of changes attributable to an intervention, both in terms of immediate (level change) and sustained (slope change) effects, while accounting for pre-intervention secular trends.ResultsOverall, in the 60 months of the study there were 84,368 cases. Compared to the pre-pandemic years, statistically significant 35 and 19% decreases in hospitalizations were observed during school closure and in the following mitigation measures phase, respectively. The greatest reduction was recorded for “Respiratory Diseases,” whereas the “Mental Disorders” category exhibited a significant increase during mitigation measures. ITS analysis confirms a high reduction of level change during school closure for Respiratory Diseases (HRR 0.19, 95%CI 0.08–0.47) and a similar but smaller significant reduction when mitigation measures were enacted. Level change for Mental Disorders significantly decreased during school closure (HRR 0.50, 95%CI 0.30–0.82) but increased during mitigation measures by 28% (HRR 1.28, 95%CI 0.98–1.69).ConclusionOur findings provide information on the impact of COVID-19 NPIs which may inform public health policies in future health crises, plan effective control and preventative interventions and target resources where needed.

BackgroundThe use of Non-Pharmaceutical Interventions (NPIs), such as lockdowns, social distancing and school closures, against the COVID-19 epidemic is debated, particularly for the possible negative effects on vulnerable populations, including children and adolescents. This study therefore aimed to quantify the impact of NPIs on the trend of pediatric hospitalizations during 2 years of pandemic compared to the previous 3 years, also considering two pandemic phases according to the type of adopted NPIs.MethodsThis is a multicenter, quasi-experimental before-after study conducted in 12 hospitals of the Emilia-Romagna Region, Northern Italy, with NPI implementation as the intervention event. The 3 years preceding the beginning of NPI implementation (in March 2020) constituted the pre-pandemic phase. The subsequent 2 years were further subdivided into a school closure phase (up to September 2020) and a subsequent mitigation measures phase with less stringent restrictions. School closure was chosen as delimitation as it particularly concerns young people. Interrupted Time Series (ITS) regression analysis was applied to calculate Hospitalization Rate Ratios (HRR) on the diagnostic categories exhibiting the greatest variation. ITS allows the estimation of changes attributable to an intervention, both in terms of immediate (level change) and sustained (slope change) effects, while accounting for pre-intervention secular trends.ResultsOverall, in the 60 months of the study there were 84,368 cases. Compared to the pre-pandemic years, statistically significant 35 and 19% decreases in hospitalizations were observed during school closure and in the following mitigation measures phase, respectively. The greatest reduction was recorded for “Respiratory Diseases,” whereas the “Mental Disorders” category exhibited a significant increase during mitigation measures. ITS analysis confirms a high reduction of level change during school closure for Respiratory Diseases (HRR 0.19, 95%CI 0.08–0.47) and a similar but smaller significant reduction when mitigation measures were enacted. Level change for Mental Disorders significantly decreased during school closure (HRR 0.50, 95%CI 0.30–0.82) but increased during mitigation measures by 28% (HRR 1.28, 95%CI 0.98–1.69).ConclusionOur findings provide information on the impact of COVID-19 NPIs which may inform public health policies in future health crises, plan effective control and preventative interventions and target resources where needed.