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In order to assist higher education institutions and their students during the pandemic, the federal government established the Higher Education Emergency Relief (HEER) fund, which directed over $75 billion to institutions of higher education - including nearly $25 billion to community colleges - over a three-year period. Researchers at the Community College Research Center (CCRC), the Public Policy Institute of California (PPIC), and Wheelhouse: The Center for Community College Leadership and Research at the University of California, Davis (Wheelhouse) partnered through the Accelerating Recovery in Community Colleges (ARCC) Network to understand how community colleges used HEER funds to support their students and institutions during the pandemic.

This data collection contains responses from a survey of 170 community colleges across six states: California, Michigan, New York, Ohio, Tennessee, and Texas. This institutional survey of pandemic relief spending and recovery strategies attempted to answer the following research questions:

1. • How did colleges use HEER student and institutional aid?
   • How did colleges target specific populations for HEER-funded student supports?
   • What do colleges' expenditure patterns reveal about how student and institutional needs changed over time?
   • How successful did colleges perceive HEER funds to be in meeting student and institutional needs during the pandemic?
   • What do colleges' concerns about the end of HEER funds reveal about how to prioritize future funding efforts?
   • In what ways did colleges' experiences with HEER funds vary based on institutional characteristics?

The resulting dataset provides insight into the specific pandemic recovery activities colleges implemented, colleges' perceptions of how successful funds were in addressing student and institutional needs during the pandemic, and what institutional needs were not met by aid.

The research team based their new method on an existing method called Bayesian Additive Regression Trees, or BART. To test the new method, the team used data created by a computer program to look like real patient data. Then they compared the new method with current methods under different scenarios. Each scenario included three treatments. The team changed the total number of patients, the number of patients who took each treatment, and how alike or different the patients were who took each treatment. Across all scenarios, the team predicted the average treatment effect for all patients and for only patients who received a treatment.

Next, the research team used the new method with real data from patients with lung cancer who were receiving care in New York City hospitals. The team compared three types of surgery: open chest, robotic assisted, and video assisted. The team looked at the effects of each type of surgery on four health outcomes: breathing problems; length of hospital stay after surgery; stay in an intensive care unit, or ICU; and the need to return to the hospital.

Patients, doctors, and researchers helped design the study.

The overall goal for this project was to reduce the incidence of COVID-19, hospitalization, and mortality among adults with serious mental illness (SMI) and intellectual disabilities/developmental disabilities (IDD) in congregate living settings (i.e., group homes) in Massachusetts, as well as to reduce COVID-19 incidence among staff who work in these settings. The research team was guided by two comparative effectiveness questions:

1. With the goal of prioritizing and making actionable best practices available as resources, what is the comparative effectiveness of various types and intensities of preventative interventions (e.g., screening, isolation, contact tracing, hand hygiene, physical distancing, use of face masks) in reducing rates of COVID-19, related hospitalizations, and related mortality in this population?
2. With the goal of effectively implementing best practices, what is the most effective implementation strategy to reduce rates of COVID-19 in this population: using tailored best practices (TBP) with SMI/IDD residents and staff of group homes in mind, or general best practices (GBP) from state and federal standard guidelines for all congregate care settings?

The specific aims of this study were as follows:

Aim 1a. Synthesize existing baseline data collected by 6 state behavioral health agencies on COVID-19 rates, hospitalization, mortality, and use of infection prevention practices.

Aim 1b. Collect stakeholder input via surveys and virtual focus groups on staff and resident experiences and on barriers/facilitators to implementing recommended preventative practices.

Aims 2a and 2b. Determine the comparative effectiveness of various COVID-19 preventative practices by (Aim 2a) using a validated simulation model to estimate COVID-19 spread in group homes and (Aim 2b) obtaining stakeholder input on prioritizing and defining tailored best practices for implementation.

Aim 3. Compare the effectiveness of TBPs with GBPs by using a hybrid effectiveness-implementation cluster randomized controlled trial.

Data collected to answer Aims 1 and 2 served as the foundation for designing the Aim 3 trial. Data for the trial were collected in 3-month intervals beginning January 2021 (baseline) until October 2022 (15-month follow-up). Residents and staff were sampled from approximately 400 group homes. Primary implementation outcome measures were COVID-19 vaccination rates and fidelity scores. The primary effectiveness outcome measure was COVID-19 infection.

Notes: This collection contains only data from Aim 1a and Aim 3. Throughout the data and documentation, "intellectual and/or developmental disabilities" is abbreviated as both IDD and ID/DD.

Over the past two decades, as the proportion of older Americans has increased, so too have instances of elder abuse, including physical, emotional, and sexual abuse; financial exploitation; and caregiver neglect. The most recent national survey estimates show at least 1 in 10 community-residing older adults experience elder abuse each year, which translates to over 7 million Americans annually. Rates of abuse are magnified for older adults with the least financial and social resources, including those with low incomes, living in isolated rural communities, and facing structural barriers such as systemic racism. Emerging research on the COVID-19 pandemic prompts even greater concern for elder abuse: the virus has disproportionately affected older adults, resulting in increased social isolation, physical health impairment, and exposure to COVID-related fraud.

Recognizing the urgent need to develop and rigorously evaluate programs aimed at preventing elder abuse, the US Department of Justice's National Institute of Justice funded a demonstration from 2017 to 2021 during which researchers from the Urban Institute and practitioners at the Phoenix-based Area Agency on Aging, Region One ("the Area Agency") co-developed an elder abuse prevention program in Maricopa County, Arizona, which Urban's team then evaluated through a randomized controlled pilot study. This multiphase demonstration included an initial planning phase and a subsequent pilot study, which is the focus of this report.

The EMPOWER: Building Late-Life Resilience program is a 12-week in-home intervention, with one-hour weekly visits designed to empower community-residing older adults with the resiliency and resources to lead safe and healthy lives throughout the aging process. EMPOWER provides one-on-one assessments, client-centered prevention education, and needs-responsive life skills training embedded in a series of cognitive reframing conversations with an experienced facilitator. The program has eight modules, each of which culminates in an action plan focused on strengthening a client's internal assets and identifying sources of positive social support. Caseworkers facilitate motivational discussions centered on clients' self-identified goals and action planning, with the aim of optimizing clients' home safety, physical health, social connectedness, and emotional and financial well-being.

People experiencing homelessness (PEH) were among the most likely to contract the novel coronavirus disease 2019 (COVID-19). Many PEH utilized high-density public places to satisfy their basic needs (e.g., soup kitchens for sustenance, public libraries for restrooms). This made it difficult for them to limit close contact with others and put them at increased risk of contracting and transmitting COVID-19. Furthermore, it was difficult to follow recommended protective measures--such as handwashing and social distancing--when living in shelters or on the streets.

PEH were at higher risk of COVID-19 related hospitalization and death than the rest of the population. The poor living conditions of PEH accelerated aging, leading them to experience geriatric conditions and medical complications more typical of individuals 10-20 years older. They were also at increased risk of cardiovascular and respiratory disease, HIV/AIDS, and diabetes, all conditions that increase vulnerability to serious COVID-19-related complications and death. These risks were compounded by the fact that PEH also faced significant barriers to accessing quality health care. In the absence of protective action, it was estimated that more than 21,000 PEH would require hospitalization due to COVID-19, more than 7,000 would require critical care, and nearly 3,500 would die.

Consequently, the COVID-19 pandemic made housing and health care for PEH one of the top priorities for the U.S. health care and public health systems. State and local governments across the country used federal relief funds to allocate private hotel rooms as protective shelter for vulnerable PEH. In Los Angeles County (LAC), which contains the largest unsheltered homeless population in the nation, 2,400 PEH were placed in hotels. COVID-19 response plans included accommodating up to 15,000 PEH in hotels who would then be moved to permanent housing in 90 days. This rapid push into housing amid a pandemic necessitated a delicate balance between social distancing and maintaining patients' basic needs, continuity of existing care, and personal and social well-being.

Permanent supportive housing (PSH)--programs that provide immediate access to independent living situations coupled with support services--is the most effective approach for serving PEH. Numerous studies have demonstrated PSH's effectiveness in improving housing retention, quality of life, and HIV outcomes. Though evidence concerning its impact on other health outcomes, health behaviors, and health care utilization is limited, the National Academies of Sciences, Engineering, and Medicine has nonetheless recognized PSH as extremely beneficial for PEH's health. COVID-19 was what this organization termed a "housing-sensitive condition"--one whose transmissibility, course, and medical management are particularly influenced by homelessness. Consequently, the National Alliance to End Homelessness recommended the use of PSH as part of its framework to address COVID-19 and homelessness.

However, significant questions remain about what types of PSH programs can best address COVID-19-related risk and promote patient-centered outcomes at a time of social and community disruption. There are two distinct approaches to implementing PSH: place-based (PB) PSH, or single-site housing placement in a congregate residence with on-site services, and scattered-site (SS) PSH, which uses apartments rented from a private landlord to house clients while providing mobile case management services. The strengths and weaknesses of these two approaches remain largely unknown but may have direct implications for adherence to COVID-19 prevention protocols and other health-related outcomes.

The COVID-19 Citizen Science (CCS) Study was launched early in the pandemic to collect patient-reported information about exposures, risk behaviors and outcomes relevant to the pandemic. The Patient-Centered Outcomes Research Institute (PCORI) funded the research team to expand recruitment into CCS using PCORnet, the National Patient-Centered Clinical Research Network, and to use the resulting data to compare the patient-reported impact of pandemic associated policies. The research team systematically collected pandemic-associated policies enacted by counties across the United States (focusing in areas where there were many CCS participants), and to do so on a weekly basis from the beginning of the pandemic using publicly available sources.

Researchers combined data from various sources to answer two primary research questions (RQ):

1. What is the comparative impact of different shelter-in-place/reopening policies, overall and in vulnerable populations, on patient-reported financial insecurity, mental health, and other subjective outcomes important to patients?
2. What is the comparative effectiveness of county-level containment and mitigation strategies at achieving timely access to COVID-19 vaccination, testing, healthcare, information and contact tracing?

The research team collected patient-reported data from the CCS study and policy data from the U.S COVID-19 County Policy (UCCP) database. Electronic health record (EHR) data were also available from some participants recruited from health systems located across 7 U.S. states who consented and authorized use of these data for the study. Data for these participants were extracted from the PCORnet Common Data Model (CDM). Additional county-level contextual variables were included in analysis.

This collection contains CCS survey data on patient-reported anxiety with county-level policies data (DS1), respondent demographics (DS2), baseline survey results (DS3), daily (DS4) and weekly (DS5) COVID-19 symptoms reports, COVID-19 vaccination surveys repeated monthly (DS6) as well as a one-time vaccination survey (DS7), and pandemic impacts check-in surveys (DS8). CDM datasets include logistic regression model outcomes to predict study enrollment among all invited participants (DS9), codes for immunizations (DS10), laboratory tests (DS11), and procedures (DS12). County-level variables are also available for years 2021 (DS13) and 2023 (DS14).

The University of Pennsylvania Health System (Penn Medicine) developed COVID Watch, an automated text message-based, remote monitoring program with 24/7 clinical support. Remote outpatient monitoring of patients with COVID-19 became needed because patients with SARS-CoV-2 infection can decline rapidly and unpredictably, and because of their own limited capacity to manage acute symptoms and concerns about staff safety, office-based outpatient practices often redirect patients with confirmed or suspected COVID-19 to hospitals. As a result, emergency departments (EDs) and hospitals became overwhelmed during surge periods of high community incidence rates and prevalence. Remote monitoring has the potential to facilitate ED- and hospital-level care for patients who require it while supporting access to care for patients who can safely remain at home.

This study compared outcomes for patients enrolled in COVID Watch with those of patients who were eligible to enroll but received usual care, with the hypothesis that enrollment in COVID Watch was associated with reduced mortality. The present research examined whether patients with COVID-19 who were enrolled in COVID Watch experienced better health outcomes compared with usual care (Aim 1) and whether augmenting COVID Watch with at-home monitoring of SpO2 (blood-oxygen saturation) improves patient outcomes (Aim 2).

In the context of COVID-19, RAND and the Robert Wood Johnson Foundation partnered again to build from the National Survey of Health Attitudes to implement a longitudinal survey to understand how these health views and values have been affected by the experience of the pandemic, with particular focus on populations deemed vulnerable or underserved, including people of color and those from low- to moderate-income backgrounds.

The questions in this COVID-19 survey focused specifically on experiences related to the pandemic (e.g., financial, physical, emotional), how respondents viewed the disproportionate impacts of the pandemic, whether and how respondents' views and priorities regarding health actions and investments are changing (including the roles of government and the private sector), and how general values about such issues as freedom and racism may be related to pandemic views and response expectations.

This study includes the results for Wave 4 for the general population.

Demographic information includes sex, marital status, household size, race and ethnicity, family income, employment status, age, and census region.

In the context of COVID-19, RAND and the Robert Wood Johnson Foundation (RWJF) partnered again to build from the National Survey of Health Attitudes to implement a longitudinal survey to understand how health views and values have been affected by the experience of the pandemic, with particular focus on populations deemed vulnerable or underserved, including people of color and those from low to moderate-income backgrounds.

The questions in this COVID-19 survey focused specifically on experiences related to the pandemic (e.g., financial, physical, emotional), how respondents viewed the disproportionate impacts of the pandemic, whether and how respondents' views and priorities regarding health actions and investments are changing (including the roles of government and the private sector), and how general values about such issues as freedom and racism may be related to pandemic views and response expectations. Some questions used in the NSHA are fielded in this COVID-19 survey while others are newly used from other COVID-19 surveys or newly developed for this effort. The study is a longitudinal study, collecting data in four waves. The study also included 2 populations: A sample of populations at greater risk, and a general population sample. This study includes the results for Wave 4 for populations at greater risk. The questions in the surveys were largely similar across all four waves.

Demographic info includes sex, marital status, household size, race and ethnicity, family income, employment status, age, and census region.

The potential impacts of the COVID-19 pandemic in Ethiopia are expected to be severe on Ethiopian households' welfare. To monitor these impacts on households, the team selected a subsample of households that had been interviewed for the Living Standards Measurement Study (LSMS) in 2019, covering urban and rural areas in all regions of Ethiopia. The 15-minute questionnaire covers a series of topics, such as knowledge of COVID and mitigation measures, access to routine healthcare as public health systems are increasingly under stress, access to educational activities during school closures, employment dynamics, household income and livelihood, income loss and coping strategies, and external assistance.

The survey is implemented using Computer Assisted Telephone Interviewing, using a modular approach, which allows for modules to be dropped and/or added in different waves of the survey. Survey data collection started at the end of April 2020 and households are called back every three to four weeks for a total of seven survey rounds to track the impact of the pandemic as it unfolds and inform government action. This provides data to the government and development partners in near real-time, supporting an evidence-based response to the crisis.

The sample of households was drawn from the sample of households interviewed in the 2018/2019 round of the Ethiopia Socioeconomic Survey (ESS). The extensive information collected in the ESS, less than one year prior to the pandemic, provides a rich set of background information on the COVID-19 High Frequency Phone Survey of households which can be leveraged to assess the differential impacts of the pandemic in the country.

The World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley conducted the Kenya COVID-19 Rapid Response Phone Survey (RRPS) to track the socioeconomic impacts of the COVID-19 pandemic and the recovery from it to provide timely data to inform policy. This collection contains information from seven waves of the COVID-19 RRPS, which was part of a panel survey that targeted Kenyan nationals and started in May 2020. The same households were interviewed every two months for five survey rounds in the first year of data collection and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques. Sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. The "WAVE" variable represents in which wave the households were interviewed in. All waves of this survey included information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge and vaccinations.

The data contain information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority were randomly selected. The samples covered urban and rural areas and were designed to be representative of the population of Kenya using cell phones. The sample size for each completed wave was:

• Wave 1: 4,061 Kenyan households
• Wave 2: 4,492 Kenyan households
• Wave 3: 4,979 Kenyan households
• Wave 4: 4,892 Kenyan households
• Wave 5: 5,854 Kenyan households
• Wave 6: 5,765 Kenyan households
• Wave 7: 5,633 Kenyan households

The collection is organized into three levels. The first level is the Household Level Data, which contains household level information. The 'HHID' variable uniquely identifies all households. The second level is the Adult Level Data, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the 'ADULT_ID' variable. The third level is the Child Level Data, which contains information for every child in the household. Each child in a household is uniquely identified by the 'CHILD_ID' variable.

Malawi High-Frequency Phone Survey COVID-19 (HFPS COVID-19) was implemented by the National Statistical Office (NSO) on a monthly basis during the period of May 2020 and June 2021. The survey is part of a World Bank-supported global effort to support countries in their data collection efforts to monitor the impacts of COVID-19. The financing for data collection and technical assistance in support of the Malawi HFPS COVID-19 is provided by the United States Agency for International Development (USAID) and the World Bank.

The COVID-19 Trends and Impact Survey (CTIS) was conducted by the Delphi Group at Carnegie Mellon University (CMU) in the United States (US) and by the University of Maryland (UMD) Social Data Science Center (SoDa) globally, in partnership with Meta. CTIS was a daily repeated cross-sectional survey that ran continuously starting April 6, 2020 in the US and starting April 23, 2020 globally. Both surveys concluded data collection on June 25, 2022. CTIS collected data in 200+ countries and territories, including 114 where Meta provided survey weights. The sampling frame was Facebook users aged 18 years or older who have been active on the platform in the last month. Sampled Facebook users saw the invitation at the top of their Feed, but the surveys were collected by the universities using Qualtrics. Meta neither collected nor received survey responses. The sample was stratified by subnational regions. Respondents were sampled as frequently as every month and as infrequently as every six months, depending on the population density of the subnational region in which they lived. Due to the minimum sampling frequency, pooled analyses should not combine more than a month of data. There were 12 versions of the survey questionnaires. The Delphi US CTIS was translated into 8 languages. The UMD Global CTIS was translated into 66 languages.

This collection is comprised of three categories of data:

a. Individual-level microdata files, which will be available to eligible academic and nonprofit researchers with fully executed Data Use Agreements (DUAs).

b. Daily aggregate estimates at the country and subnational region levels disseminated via public APIs at CMU and UMD.

c. Weekly and monthly aggregate estimates broken out by respondent characteristics (e.g., age, gender, vaccination status) at the country and subnational administrative level-1 region-level disseminated via publicly available CSV-formatted contingency tables.

This collection currently only contains the aggregate data, contingency tables and associated documentation. The microdata are forthcoming.

The United States COVID-19 Trends and Impact Survey (CTIS) was a voluntary survey of Facebook users in the United States conducted from April 2020 to June 2022. CTIS was intended to aid in pandemic forecasting and response at fine spatiotemporal detail. Through collaboration with Meta, it randomly sampled Facebook active users at a rate sufficient to provide roughly 35,000 responses per day, on average. Survey questions covered topics including COVID-like symptoms, behavior (such as social distancing), COVID testing, mental health, health-related beliefs, trust in officials and information sources, schooling, vaccination acceptance and hesitancy, and related subjects. Respondents provided their ZIP code. Demographic variables include age, gender, education, race/ethnicity, and occupation. Meta generated survey weights to correct for non-response and to match the US adult population age and gender distribution.

The 27 datasets make up the microdata. Users should see the Microdata User Guide for documentation on the use and interpretation of the microdata files.

Two zip files are available for public download: a monthly data zip file and a weekly data zip file. These include the aggregate data. To access these files, go to the "Download" tab and select "Other." Ensure you have enough storage space before proceeding, as the files are large.

The Detroit Metro Area Communities Study (DMACS) is a panel survey of Detroit residents aged 18 and older. The original panel of respondents was drawn from an address-based probability sample of all occupied Detroit households in 2016 and has since been refreshed through additional address-based sampling annually. The 13th survey wave, collected between June 2, 2021 and July 9, 2021, invited 2,713 previously-enrolled panelists to participate in a self-administered or interviewer-administered survey. Topics include experience with COVID-19; COVID-19 vaccine receipt, attitudes and trust; employment and economic precarity; neighborhood satisfaction; neighborhood blight; housing repair; as well as crime, safety and policing.

The Detroit Metro Area Communities Study (DMACS) is a panel survey of Detroit residents aged 18 and older. The original panel of respondents was drawn from an address-based probability sample of all occupied Detroit households in 2016 and has since been refreshed through additional address-based sampling annually. Between November 3 and December 15, 2021, 2,662 previously-enrolled panelists were invited to participate in a self-administered online or interviewer-administered telephone survey. A total of 1,900 Detroit residents completed the survey, yielding an overall response rate of 72% (using AAPOR Response Rate 1).

Topics include experience with COVID-19; COVID-19 vaccine receipt, attitudes and trust; employment and economic precarity; neighborhood satisfaction; neighborhood change; as well as healthcare usage; the Child Tax Credit; and Digital Inclusion.

The Detroit Metro Area Communities Study (DMACS) is a panel survey of Detroit residents aged 18 and older. The original panel of respondents was drawn from an address-based probability sample of all occupied Detroit households in 2016 and has since been refreshed through additional address-based sampling annually. The 18th survey wave, collected between November 2, 2023 and December 19, 2023, invited 3,065 previously-enrolled panelists to participate in a self-administered or interviewer-administered survey.

Topics included: transportation; health; long COVID-19 symptoms; mental health; grocery shopping routines; household finances; inequality; new technologies; drones; non-degree training program; employment.

This study includes 19 interview transcripts. The overall intent of the interviews was to gather information that could be used to develop and improve a single reporting website for overdose tracking and naloxone distribution metrics that would generate monthly reports to the State of Texas. The goal of this study was to develop and implement a digital ecosystem to improve opioid overdose reporting, tracking, and prevention across Texas. The study also aimed to understand the relationship between substance use behavior, stress response, and social support structures during the COVID-19 pandemic.

The objective of this study is to validate the Institutional Medical Mistrust Scale (IMMS) using a large, national population to better understand issues of public trust in healthcare and government organizations. The aims of this study are: (1) conduct a national population survey using the IMMS; (1a) examine the influence that healthcare organizations and governing institutions at the local, state, and federal level have on medical mistrust during the COVID-19 pandemic in the United States; (1b) test the psychometrics of the IMMS in a large national survey; (2) test the IMMS in a national population with intentional oversampling of African American/Black, Latinx, and chronic disease respondents in the United States.

The endpoints for this study are divided into psychosocial measures as well as physical measures including: (1) measurement of institutional medical mistrust among health care and local/state and federal government organizations; (2) mental and physical health; (3) vaccine uptake or hesitation; (4) factors associated with vaccine uptake or hesitation.

The Expanding Children's Early Learning (ExCEL) Quality Study: Improving Preschool and Early Care and Education (ECE) Instruction through Curricula, Coaching, and Training was funded by Arnold Ventures and conducted by MDRC and its subcontractors, MEF Associates and RTI International. The study consisted of two phases: ExCEL Quality (EQ; 2019-2020) and ExCEL Quality Continuation (EQC; 2020-2021). This collection contains analysis datasets with calculated measures at the child (DS2) and classroom (DS3) levels, a samples dataset for each phase (EQ = DS1, EQC = DS20), and raw data organized by study phase and data source.

ExCEL Quality

In the EQ study, the research team conducted an experimental study testing two interventions that consisted of curricular and professional development support and that targeted different dimensions of classroom quality, namely:

• A whole-child intervention that targets structural and interactional aspects of classroom quality, using The Creative Curriculum for Preschool, 6th Edition
• A domain-specific intervention with a specific scope and sequence that targets instructional aspects of classroom quality, using Connect4Learning, 1st Edition

ECE centers were selected across four U.S. localities in Head Start and community-based child care settings and were randomly assigned to one of three research conditions (Creative Curriculum, Connect4Learning, or preschool-as-usual). The project was originally designed to assess the impacts of each of the interventions on the content of instruction, teacher practices, classroom quality, and child outcomes, as well as to collect program implementation data. However, because of the COVID-19 pandemic that began in March of the 2019-2020 school year, adjustments were made to the guiding questions and research aims, data sources, and analysis.

ExCEL Quality Continuation

The EQC Study was a non-experimental study, aiming to understand ECE teachers' and administrators' experiences during the COVID-19 pandemic (2020-2021 school year). The sample was a subset of the centers that participated in EQ in the 2019-2020 school year (n=26), plus five newly recruited centers. EQC provided stress management workshops and curricular support (Creative Curriculum or Connect4Learning) to participating centers regardless of their original research condition in EQ.

The National Association of County and City Health Officials' (NACCHO) Forces of Change Survey was developed as an evolution to NACCHO's Job Losses and Program Cuts Surveys (also known as the Economic Surveillance Surveys), which measured the impact of the economic recession on local health departments' (LHDs) budgets, staff, and programs.

The Forces of Change Survey continues to measure changes in LHD budgets, staff, and programs and assess more broadly the impact of forces affecting change in LHDs. For the 2020 Forces of Change survey, the core set of questions were distributed to a total of 2392 LHDs in the United States, with a stratified random sample of 905 LHDs receiving the module questionnaire.

More specifically, the survey collected information about LHD staffing levels, workforce reductions, and changes in budget sizes; provided services or functions; changes in the level of service delivery; billing for clinical services; efforts to help people enroll in health insurance from exchanges under the Affordable Care Act; awareness of and involvement in the State Innovation Models Initiative; participation in the Public Health Accreditation Board's national accreditation program for LHDs; and whether LHDs are part of a combined health and human services agency.