Note: After November 1, 2024, this dataset will no longer be updated due to a transition in NHSN Hospital Respiratory Data reporting that occurred on Friday, November 1, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

Due to a recent update in voluntary NHSN Hospital Respiratory Data reporting that occurred on Wednesday, October 9, 2024, reporting levels and other data displayed on this page may fluctuate week-over-week beginning Friday, October 18, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient. 
. This dataset represents weekly respiratory virus-related hospitalization data and metrics aggregated to national and state/territory levels reported during two periods: 1) data for collection dates from August 1, 2020 to April 30, 2024, represent data reported by hospitals during a mandated reporting period as specified by the HHS Secretary; and 2) data for collection dates beginning May 1, 2024, represent data reported voluntarily by hospitals to CDC’s National Healthcare Safety Network (NHSN). NHSN monitors national and local trends in healthcare system stress and capacity for up to approximately 6,000 hospitals in the United States. Data reported represent aggregated counts and include metrics capturing information specific to COVID-19- and influenza-related hospitalizations, hospital occupancy, and hospital capacity. Find more information about reporting to NHSN at: https://www.cdc.gov/nhsn/covid19/hospital-reporting.html

Source: COVID-19 hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN).

• Data source description(updated October 18, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or ‘COVID-19 hospital data’) are reported to HHS through CDC’s National Healthcare Safety Network based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data are voluntarily reported to NHSN as of May 1, 2024 until November 1, 2024, at which time CMS will require acute care and critical access hospitals to electronically report information via NHSN about COVID-19, Influenza, and RSV, hospital bed census and capacity, and limited patient demographic information, including age. Data for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary. Data for collection dates May 1, 2024, and onwards represent data reported voluntarily to NHSN; as such, data included represents reporting hospitals only for a given week and might not be complete or representative of all hospitals. NHSN monitors national and local trends in healthcare system stress and capacity for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient.
• Data quality: While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Data since December 1, 2020, have had error correction methodology applied; data prior to this date may have anomalies that are not yet resolved. Data prior to August 1, 2020, are unavailable.
• Metrics and inclusion criteria: Many hospital subtypes, including acute care and critical access hospitals, are included in the metric calculations included in this dataset. Psychiatric, rehabilitation, and religious non-medical hospital types, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are excluded from calculations. For a given metric calculation, hospitals that reported those data at least one day during a given week are included.
• Find full details on NHSN hospital data reporting guidance at https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Notes: May 10, 2024: Due to missing hospital data for the April 28, 2024 through May 4, 2024 reporting period, data for Commonwealth of the Northern Mariana Islands (CNMI) are not available for this period in the Weekly NHSN Hospitalization Metrics report released on May 10, 2024.

May 17, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), Minnesota (MN), and Guam (GU) for the May 5,2024 through May 11, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 1, 2024.

May 24, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), and Minnesota (MN) for the May 12, 2024 through May 18, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 24, 2024.

May 31, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Virgin Islands (VI), Massachusetts (MA), and Minnesota (MN) for the May 19, 2024 through May 25, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 31, 2024.

June 7, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Virgin Islands (VI), Massachusetts (MA), Guam (GU), and Minnesota (MN) for the May 26, 2024 through June 1, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 7, 2024.

June 14, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), and Minnesota (MN) for the June 2, 2024 through June 8, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 14, 2024.

June 21, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Guam (GU), Virgin Islands (VI), and Minnesota (MN) for the June 9, 2024 through June 15, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 21, 2024.

June 28, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 16, 2024 through June 22, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 28, 2024.

July 5, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 23, 2024 through June 29, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 5, 2024.

July 12, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 30, 2024 through July 6 , 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 12, 2024.

July 19, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 7, 2024 through July 13, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 19, 2024.

July 26, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 13, 2024 through July 20, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 26, 2024.

August 2, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), West Virginia (WV), and Minnesota (MN) for the July 21, 2024 through July 27, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 2, 2024.

August 9, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), Guam (GU), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 28, 2024 through August 3, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 9, 2024.

August 16, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the August 4, 2024 through August 10, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 16, 2024.

August 23, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the August 11, 2024 through August 17, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics

Morocco MA: Coverage: Social Safety Net Programs: Poorest Quintile: % of Population data was reported at 50.107 % in 2009. Morocco MA: Coverage: Social Safety Net Programs: Poorest Quintile: % of Population data is updated yearly, averaging 50.107 % from Dec 2009 (Median) to 2009, with 1 observations. Morocco MA: Coverage: Social Safety Net Programs: Poorest Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank.WDI: Social Protection. Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Note: After November 1, 2024, this dataset will no longer be updated due to a transition in NHSN Hospital Respiratory Data reporting that occurred on Friday, November 1, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

Due to a recent update in voluntary NHSN Hospital Respiratory Data reporting that occurred on Wednesday, October 9, 2024, reporting levels and other data displayed on this page may fluctuate week-over-week beginning Friday, October 18, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient. 

This dataset represents weekly respiratory virus-related hospitalization data and metrics aggregated to national and state/territory levels reported during two periods: 1) data for collection dates from August 1, 2020 to April 30, 2024, represent data reported by hospitals during a mandated reporting period as specified by the HHS Secretary; and 2) data for collection dates beginning May 1, 2024, represent data reported voluntarily by hospitals to CDC’s National Healthcare Safety Network (NHSN). NHSN monitors national and local trends in healthcare system stress and capacity for up to approximately 6,000 hospitals in the United States. Data reported represent aggregated counts and include metrics capturing information specific to COVID-19- and influenza-related hospitalizations, hospital occupancy, and hospital capacity. Find more information about reporting to NHSN at: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

Source: COVID-19 hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN).

• Data source description (updated October 18, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or ‘COVID-19 hospital data’) are reported to HHS through CDC’s National Healthcare Safety Network based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data are voluntarily reported to NHSN as of May 1, 2024 until November 1, 2024, at which time CMS will require acute care and critical access hospitals to electronically report information via NHSN about COVID-19, Influenza, and RSV, hospital bed census and capacity, and limited patient demographic information, including age. Data for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary. Data for collection dates May 1, 2024, and onwards represent data reported voluntarily to NHSN; as such, data included represents reporting hospitals only for a given week and might not be complete or representative of all hospitals. NHSN monitors national and local trends in healthcare system stress and capacity for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient. 
• Data quality: While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Data since December 1, 2020, have had error correction methodology applied; data prior to this date may have anomalies that are not yet resolved. Data prior to August 1, 2020, are unavailable.
• Metrics and inclusion criteria: Many hospital subtypes, including acute care and critical access hospitals, are included in the metric calculations included in this dataset. Psychiatric, rehabilitation, and religious non-medical hospital types, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are excluded from calculations. For a given metric calculation, hospitals that reported those data at least one day during a given week are included.
• Find full details on NHSN Hospital Respiratory Data (HRD) reporting guidance, including additional information on bed type definitions at https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

Notes: May 10, 2024: Due to missing hospital data for the April 28, 2024 through May 4, 2024 reporting period, data for Commonwealth of the Northern Mariana Islands (CNMI) are not available for this period in the Weekly NHSN Hospitalization Metrics report released on May 10, 2024.

May 17, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), Minnesota (MN), and Guam (GU) for the May 5,2024 through May 11, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 1, 2024.

May 24, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), and Minnesota (MN) for the May 12, 2024 through May 18, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 24, 2024.

May 31, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Virgin Islands (VI), Massachusetts (MA), and Minnesota (MN) for the May 19, 2024 through May 25, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 31, 2024.

June 7, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Virgin Islands (VI), Massachusetts (MA), Guam (GU), and Minnesota (MN) for the May 26, 2024 through June 1, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 7, 2024.

June 14, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), and Minnesota (MN) for the June 2, 2024 through June 8, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 14, 2024.

June 21, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Guam (GU), Virgin Islands (VI), and Minnesota (MN) for the June 9, 2024 through June 15, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 21, 2024.

June 28, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 16, 2024 through June 22, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 28, 2024.

July 5, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 23, 2024 through June 29, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 5, 2024.

July 12, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 30, 2024 through July 6, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 12, 2024.

July 19, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 7, 2024 through July 13, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 19, 2024.

July 26, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 13, 2024 through July 20, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 26, 2024.

August 2, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), West Virginia (WV), and Minnesota (MN) for the July 21, 2024 through July 27, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 2, 2024.

August 9, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), Guam (GU), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 28, 2024 through August 3, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 9, 2024.

August 16, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the August 4, 2024 through August 10, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 16, 2024.

August 23, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the August 11, 2024 through August 17, 2024 reporting period are not available for the Weekly

IntroductionAccess to COVID-19 testing has been inequitable and misaligned with community need. However, community health centers have played a critical role in addressing the COVID-19 testing needs of historically disadvantaged communities. The aim of this paper is to explore the perceptions of COVID-19 testing barriers in six Massachusetts communities that are predominantly low income and describe how these findings were used to build tailored clinical-community strategies to addressing testing inequities.MethodsBetween November 2020 and February 2021, we conducted 84 semi-structured qualitative interviews with 107 community health center staff, community partners, and residents. Resident interviews were conducted in English, Spanish, Vietnamese, and Arabic. We used a 2-phase framework analysis to analyze the data, including deductive coding to facilitate rapid analysis for action and an in-depth thematic analysis applying the Social Ecological Model.ResultsThrough the rapid needs assessment, we developed cross-site suggestions to improve testing implementation and communications, as well as community-specific recommendations (e.g., locations for mobile testing sites and local communication channels). Upstream barriers identified in the thematic analysis included accessibility of state-run testing sites, weak social safety nets, and lack of testing supplies and staffing that contributed to long wait times. These factors hindered residents' abilities to get tested, which was further exacerbated by individual fears surrounding the testing process and limited knowledge on testing availability.DiscussionOur rapid, qualitative approach created the foundation for implementing strategies that reached underserved populations at the peak of the COVID-19 pandemic in winter 2021. We explored perceptions of testing barriers and created actionable summaries within 1–2 months of data collection. Partnering community health centers in Massachusetts were able to use these data to respond to the local needs of each community. This study underscores the substantial impact of upstream, structural disparities on the individual experience of COVID-19 and demonstrates the utility of shifting from a typical years' long research translation process to a rapid approach of using data for action.

Morocco MA: Coverage: Social Safety Net Programs: 4th Quintile: % of Population data was reported at 32.544 % in 2009. Morocco MA: Coverage: Social Safety Net Programs: 4th Quintile: % of Population data is updated yearly, averaging 32.544 % from Dec 2009 (Median) to 2009, with 1 observations. Morocco MA: Coverage: Social Safety Net Programs: 4th Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank.WDI: Social Protection. Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Physical function is the physical ability to fulfill one’s daily roles and responsibilities. Poor physical function is detrimental to health and income-generating activities. Unfortunately, there is a lack of validated methods to measure physical function in adult women in low- and middle-income countries, including Ethiopia, the locus of this study. This study evaluated the feasibility, reliability, and validity of physical tests, including the sit-to-stand (STS) and usual gait speed (UGS) and a context-appropriate instrumental activities of daily living (IADL) survey. The results of the STS were used to calculate a muscle quality index (MQI, STS accounting for body mass and leg length). Feasibility was ascertained qualitatively based on reports from the enumerators on their ability to administer the tests. Reliability was assessed by comparing the results of the tests and questions between each visit using either Cohen’s κ or Pearson’s ρ. The validity of MQI was assessed using relevant participant characteristics such as age and self-reported disability. The validity of the IADL was assessed using MQI. Study participants comprised 316 women between the ages of 18 and 45 years, living in rural Tigray, Ethiopia, who had previously participated in an impact evaluation of a safety net program. Over a one-week period, participants completed the STS and UGS tests and responded to the IADL survey questions three times. MQI was determined to be a feasible, reliable, and valid physical function test for women in rural, highland Ethiopia. UGS lacked feasibility and reliability; validity was not ascertained. The IADL questions were feasible and reliable, but validity was inconclusive. In rural Ethiopia, the MQI will be a valuable tool to develop interventions for improving physical function, which will have positive impacts on health and quality of life.

Morocco MA: Coverage: Social Safety Net Programs: % of Population data was reported at 36.589 % in 2009. Morocco MA: Coverage: Social Safety Net Programs: % of Population data is updated yearly, averaging 36.589 % from Dec 2009 (Median) to 2009, with 1 observations. Morocco MA: Coverage: Social Safety Net Programs: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank.WDI: Social Protection. Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

This dataset is the result of the community survey that was conducted to gather data at endline as a part of the impact evaluation of the Alive & Thrive (A&T) interventions in Ethiopia. The broad objective of the impact evaluation in Ethiopia is to measure the impact of A&T’s community-based interventions, delivered through the government's health extension program (HEP) platform, in the reduction of stunting and improvement of IYCF practices in two regions where the IFHP operates, namely Tigray and SNNPR (Southern Nations, Nationalities, and People’s Region). A&T is a six-year initiative to facilitate change for improved infant and young child feeding (IYCF) practices at scale in Bangladesh, Ethiopia, and Viet Nam. The goal of A&T is to reduce avoidable death and disability due to suboptimal IYCF in the developing world by increasing exclusive breastfeeding (EBF) until 6 months of age and reducing stunting of children 0-24 months of age. The impact evaluation of A&T’s community-based intervention and mass media activities applied an “adequacy design,” which involves pre- and post-intervention assessments without a non-intervention comparison group. A total of 75 enumeration areas (EAs) were randomly selected from woredas (districts) that were part of the IFHP platform for A&T in Tigray and SNNPR. Repeated cross-sectional surveys were conducted at baseline (2010) and endline (2014) in the 75 EAs. A short questionnaire was administered to community leaders to gather information on the contextual factors at the community level as well as to understand differences in characteristics across the clusters (EA) over time. One questionnaire was completed for each cluster (EA). This information at the community level is critical to control for externalities that could influence the outcome of the program. The Ethiopia endline community questionnaire provided information on the following: 1) General characteristics of the EA/kebele (population, livelihood, season of food shortage), 2) Infrastructure (access to main road, electricity, access to clean water, 3) Distance from the nearest major town, type of transportation use to reach the town, 4) Access to the nearest market, 5) Migration patterns, 6) Social and food assistance (productive safety net program, community-based nutrition program), 7) Natural disasters occurred in the area during the last year, 8) Availability and access to health and education facilities (health post, government hospital, private clinic, junior and high school, college).

Physical function is the physical ability to fulfill one’s daily roles and responsibilities. Poor physical function is detrimental to health and income-generating activities. Unfortunately, there is a lack of validated methods to measure physical function in adult women in low- and middle-income countries, including Ethiopia, the locus of this study. This study evaluated the feasibility, reliability, and validity of physical tests, including the sit-to-stand (STS) and usual gait speed (UGS) and a context-appropriate instrumental activities of daily living (IADL) survey. The results of the STS were used to calculate a muscle quality index (MQI, STS accounting for body mass and leg length). Feasibility was ascertained qualitatively based on reports from the enumerators on their ability to administer the tests. Reliability was assessed by comparing the results of the tests and questions between each visit using either Cohen’s κ or Pearson’s ρ. The validity of MQI was assessed using relevant participant characteristics such as age and self-reported disability. The validity of the IADL was assessed using MQI. Study participants comprised 316 women between the ages of 18 and 45 years, living in rural Tigray, Ethiopia, who had previously participated in an impact evaluation of a safety net program. Over a one-week period, participants completed the STS and UGS tests and responded to the IADL survey questions three times. MQI was determined to be a feasible, reliable, and valid physical function test for women in rural, highland Ethiopia. UGS lacked feasibility and reliability; validity was not ascertained. The IADL questions were feasible and reliable, but validity was inconclusive. In rural Ethiopia, the MQI will be a valuable tool to develop interventions for improving physical function, which will have positive impacts on health and quality of life.

Comparison of laboratory markers between ESKD and non-ESKD patients presenting to a large safety-net hospital in Massachusetts between March 4, 2020 and April 30, 2020.

Morocco MA: Coverage: Social Safety Net Programs: 2nd Quintile: % of Population data was reported at 43.028 % in 2009. Morocco MA: Coverage: Social Safety Net Programs: 2nd Quintile: % of Population data is updated yearly, averaging 43.028 % from Dec 2009 (Median) to 2009, with 1 observations. Morocco MA: Coverage: Social Safety Net Programs: 2nd Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank.WDI: Social Protection. Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Morocco MA: Coverage: Social Safety Net Programs: 3rd Quintile: % of Population data was reported at 39.172 % in 2009. Morocco MA: Coverage: Social Safety Net Programs: 3rd Quintile: % of Population data is updated yearly, averaging 39.172 % from Dec 2009 (Median) to 2009, with 1 observations. Morocco MA: Coverage: Social Safety Net Programs: 3rd Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank.WDI: Social Protection. Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Characteristics and clinical presentation of ESKD patients who died compared to ESKD patients who survived with COVID-19 in a large safety-net hospital in Massachusetts between March 4, 2020 and April 30, 2020.

Morocco MA: Coverage: Social Safety Net Programs: Richest Quintile: % of Population data was reported at 18.164 % in 2009. Morocco MA: Coverage: Social Safety Net Programs: Richest Quintile: % of Population data is updated yearly, averaging 18.164 % from Dec 2009 (Median) to 2009, with 1 observations. Morocco MA: Coverage: Social Safety Net Programs: Richest Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank.WDI: Social Protection. Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

aDifferences in age, gravidity and parity are significant between underweight BMI females and each BMI category; and differences in age, gravidity and parity are significant between normal BMI females and all other BMI categories by one-way ANOVA.bThe proportion of white and black women significantly decreases as the BMI category increases from normal, p for trend

Sustainability of the forest at a regional scale in landscapes dominated by non-industrial private forest (NIPF) ownership depends on the often-independent actions and behaviors of thousands of private families and individuals. These NIPF lands comprise the dominant forest ownership in many parts of the United States, and represent an important part of the greater forest landscape matrix even in parts of the region where industrial and/or public lands dominate. In northeastern states, NIPF lands can represent 75% or more of total forest area. While forest landowner attitudinal survey work in the past several decades has explored reasons for ownership, motivations, and perspectives on traditional management (e.g., do you intend to harvest in the next 10 years?), little if any study has focused on attitudes and, importantly, documented behaviors related to sustainability on their lands. Some landowner attitudes pertaining to the notion of sustainability can be inferred from earlier work (e.g., documented interest in wildlife habitat and nature, aesthetics, and privacy), but these do not directly link to sustainability or timber productivity on their own lands. As the urban-rural interface expands from metropolitan centers, though this wooded landscape may appear to be forested from the air, it no longer sustains a number of benefits upon which society has grown to depend. In particular, timber harvesting declines as a viable and sustainable land use activity. We seek to: 1. Study the decision-making process, priorities, and behaviors of different types of NIPF owners, in terms of sustainable harvesting, and sale/ development; 2. Use landscape-scale spatial data and associated demographic data to assess the extent to which such landscapes can remain sustainable producers of wood products in the face of expanding urban/ suburban influence; and 3. study sites that have sustained harvests and document the successional trajectory, in an effort to estimate the future composition of the forest landscape based on this form of human-induced disturbance. In so doing, we will identify characteristics of a NIPF landscape in which harvesting or the production of timber is no longer sustainable. Sustainable timber production from local forested landscapes plays a role in global environmental quality. In the Illusion of Preservation, Berlik et al argue for decreases in wood consumption, coupled with increases in local wood production, to avoid "exporting" the need to harvest wood from countries that have a negligible environmental safety net. The result of this is that forest that does not sustainably produce wood "at home" in effect shifts the demand to other places, thereby making local preservation an illusion.

BackgroundEconomic and supply chain shocks resulting from the COVID-19 pandemic in 2020 led to substantial increases in the numbers of individuals experiencing food-related hardship in the US, with programs aimed at addressing food insecurity like the Supplemental Nutrition Assistance Program (SNAP) and food pantries seeing significant upticks in utilization. While these programs have improved food access overall, the extent to which diet quality changed, and whether they helped mitigate diet quality disruptions, is not well understood.ObjectiveTo evaluate food insecurity, food pantry and/or SNAP participation associations with both diet quality as well as perceived disruptions in diet during the COVID-19 pandemic among Massachusetts adults with lower incomes.MethodsWe analyzed complete-case data from 1,256 individuals with complete data from a cross-sectional online survey of adults (ages 18 years and above) living in Massachusetts who responded to “The MA Statewide Food Access Survey” between October 2020 through January 2021. Study recruitment and survey administration were performed by The Greater Boston Food Bank. We excluded respondents who reported participation in assistance programs but were ineligible (n = 168), those who provided straightlined responses to the food frequency questionnaire component of the survey (n = 34), those with incomes above 300% of the federal poverty level (n = 1,427), those who completed the survey in 2021 (n = 8), and those who reported improved food insecurity (n = 55). Current dietary intake was assessed via food frequency questionnaire. Using Bayesian regression models, we examined associations between pandemic food insecurity, perceived disruption in diet, diet quality, and intakes of individual foods among those who completed a survey in 2020. We assessed interactions by pantry and SNAP participation to determine whether participation moderated these relationships.ResultsIndividuals experiencing food insecurity reported greater disruption in diet during the pandemic and reduced consumption of healthy/unhealthy foods. Pantry participation attenuated significant associations between food insecurity and lower consumption of unhealthy (b = −1.13 [95% CI −1.97 to −0.31]) and healthy foods (b = −1.07 [−1.82 to −0.34]) to null (unhealthy foods: −0.70 [−2.24 to 0.84]; healthy foods: 0.30 [−1.17 to 1.74]), whereas SNAP participation attenuated associations for healthy foods alone (from −1.07 [−1.82 to −0.34] to −0.75 [−1.83 to 0.32]). Results were robust to choice of prior as well as to alternative modeling specifications.ConclusionAmong adults with lower incomes, those experiencing food insecurity consumed less food, regardless of healthfulness, compared to individuals not experiencing food insecurity. Participation in safety-net programs, including SNAP and pantry participation, buffered this phenomenon. Continued support of SNAP and the food bank network and a focus on access to affordable healthy foods may simultaneously alleviate hunger while improving nutrition security.

Morocco MA: Coverage: Social Protection & Labour Programs: % of Population data was reported at 41.013 % in 2009. Morocco MA: Coverage: Social Protection & Labour Programs: % of Population data is updated yearly, averaging 41.013 % from Dec 2009 (Median) to 2009, with 1 observations. Morocco MA: Coverage: Social Protection & Labour Programs: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank.WDI: Social Protection. Coverage of social protection and labor programs (SPL) shows the percentage of population participating in social insurance, social safety net, and unemployment benefits and active labor market programs. Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Technology plays an increasingly important role in our daily lives. The use of technology-based healthcare apps facilitates and empowers users to use such apps and saves the burden on the public healthcare system during COVID-19. Through technology-based healthcare apps, patients can be virtually connected to doctors for medical services. This study explored users' intention and adoption of eDoctor apps in relation to their health behaviors and healthcare technology attributes among Chinese adults. Cross-sectional data were collected through social media, resulting in a total of 961 valid responses for analysis. The hybrid analysis technique of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analysis was applied. The obtained results revealed the significant influence of eDoctor apps in terms of usefulness, compatibility, accuracy, and privacy on users' intention to use eDoctor apps. Intention and product value were also found to suggestively promote the adoption of eDoctor apps. This study offered practical recommendations for the suppliers and developers of eHealth apps to make every attempt of informing and building awareness to nurture users' intention and usage of healthcare technology. Users' weak health consciousness and motivation are notable barriers that restrict their intention and adoption of the apps. Mass adoption of eDoctor apps can also be achieved through the integration of the right technology features that build the product value and adoption of eDoctor apps. The limitations of the current study and recommendations for future research are presented at the end of this paper.