View data on member enrollment, application activity, Customer Service Center statistics, and more.

Some providers may be suspended or excluded from working with MassHealth.

Find local risk levels for Eastern Equine Encephalitis (EEE) and West Nile Virus (WNV) based on seasonal testing from June to October.

After over two years of public reporting, the State Profile Report will no longer be produced and distributed after February 2023. The final release was on February 23, 2023. We want to thank everyone who contributed to the design, production, and review of this report and we hope that it provided insight into the data trends throughout the COVID-19 pandemic. Data about COVID-19 will continue to be updated at CDC’s COVID Data Tracker.

The State Profile Report (SPR) is generated by the Data Strategy and Execution Workgroup in the Joint Coordination Cell, in collaboration with the White House. It is managed by an interagency team with representatives from multiple agencies and offices (including the United States Department of Health and Human Services (HHS), the Centers for Disease Control and Prevention, the HHS Assistant Secretary for Preparedness and Response, and the Indian Health Service). The SPR provides easily interpretable information on key indicators for each state, down to the county level.

It is a weekly snapshot in time that:

• Focuses on recent outcomes in the last seven days and changes relative to the month prior
• Provides additional contextual information at the county level for each state, and includes national level information
• Supports rapid visual interpretation of results with color thresholds

Population-Attributability Using Hornik and Woolf’s Method.

Population-Attributable Fraction Using the Marginal Structural Model.

The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

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

The emerging body of research suggests the unprecedented increase in housing foreclosures and unemployment between 2007 and 2009 had detrimental effects on health. Using data from electronic health records of 105,919 patients with diabetes in Northern California, this study examined how increases in foreclosure rates from 2006 to 2010 affected weight change. We anticipated that two of the pathways that explain how the spike in foreclosure rates affects weight gain—increasing stress and declining salutary health behaviors- would be acute in a population with diabetes because of metabolic sensitivity to stressors and health behaviors. Controlling for unemployment, housing prices, temporal trends, and time-invariant confounders with individual fixed effects, we found no evidence of an association between the foreclosure rate in each patient's census block of residence and body mass index. Our results suggest, although more than half of the population was exposed to at least one foreclosure within their census block, the foreclosure crisis did not independently impact weight change.

Over the past 35 years there has been a near doubling in the worldwide prevalence of obesity. Body Mass Index (BMI) distributions in high-income societies have increasingly shifted rightwards, corresponding to increases in average BMI that are due to well-studied changes in the socioeconomic environment. However, in addition to this shift, BMI distributions have also shown marked changes in their particular shape over time, exhibiting an ongoing right-skewed broadening that is not well understood. Here, we compile and analyze the largest data set so far of year-over-year BMI changes. The data confirm that, on average, heavy individuals become lighter while light individuals become heavier year-over-year, and also show that year-over-year BMI evolution is characterized by fluctuations with a magnitude that is linearly proportional to BMI. We find that the distribution of human BMIs is intrinsically dynamic—due to the short-term variability of human weight—and its shape is determined by a...

In 2009, National Grid started a DER pilot program that offered technical support and financial incentives to qualified Massachusetts homeowners who planned and successfully completed a retrofit that incorporated the performance requirements and goals of the National Grid DER measures package. This DER measures package, developed through collaboration with Building Science Corporation (BSC), includes specific thermal and airtightness goals for the enclosure components as well as health, safety, durability, and indoor air quality requirements. By providing measures that can be included with common renovation activities such as roof replacement, window replacement, re-siding, basement remediation, and remodeling, this DER measures package is expected to have widespread application for existing homes in the New England area. The post-retrofit performance and cost ranges provided by this research project can provide concrete input for homeowners who are considering a DER. Field test data available for air tightness measured using blower door test. House 1 - Address Belchertown, MA 01007, Notes: Energy Savings: 75%, Company: Clark House 2-1 and 2 - Address (1) Brownsberger, MA 02478 and (2) Belmont, MA 02478, Notes Energy Savings: 73%, Company: Brownsberger House 3 - Address Millbury, MA 01527, Notes Energy Savings: 31%, Company: Tweedly House 4 - Address Milton, MA 02186 Notes Energy Savings: 42%, Company: Koh House 5 - Address Quincy, MA 02169, Notes Energy Savings: 57%, Company: Hall House 6-1 and 2 - Address Arlington, MA 02476, Notes Energy Savings: 55%, Company: Venable-Hwang House 7 - Address Newton, MA 02459, Notes Energy Savings: 42%, Company: Lavine House 8-1, 2, and 3 - Address Jamaica Plain, MA 02130, Notes Energy Savings: 43%, Company: Buhs House 9 - Address Northampton, MA 01060, Notes Energy Savings: 49%, Company: Wick House 10 - Address Lancaster, MA 01523, Notes Energy Savings: 40%, Company: Habitat for Humanity of North Central Massachusetts House 11 - Address Brookline, MA 02445, Notes Energy Savings: 27%, Company: Aquiline House 12 - Address Westford, MA 01886, Notes Energy Savings: 30%, Company: Atkins House 13 - Address Gloucester, MA 01930, Notes Energy Savings: 35%, Company: Cunningham

ObjectiveTo assess the association between dynamic patterns of change in body mass or fat mass and stroke.MethodsA population-based cohort of participants was selected from the China Health and Retirement Longitudinal Study (CHARLS). Body mass and fat mass were measured using obesity-related indices, including weight, body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), lipid accumulation product (LAP), and visceral adiposity index (VAI). Five changed patterns were defined: low-stable, decreasing, moderate, increasing, and persistent-high. Logistic regression analysis was performed to evaluate the association between obesity-related indices and stroke.ResultsA total of 5,834 participants were included, and the median age was 58.0 years. During a 7-years follow-up period, 354 (6.1%) participants developed stroke. The baseline levels of obesity-related indices were significantly associated with incident stroke. Regarding the dynamic change patterns, the low-stable pattern carried the lowest odds for stroke and the persistent-high pattern had the highest odds for stroke, with odds ratios of all the indices ranging from 1.73 to 3.37 (all P 

This data release contains coastal wetland synthesis products for Massachusetts, developed in collaboration with the Massachusetts Office of Coastal Zone Management. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.

Summary table updated relative to prior version to provide more useful outputs and units according to the description below. Contains 1 .csv file including the glacier health metrics used in Miles and others (2021) for all RGI glacier outlines larger than 2km2 in High Mountain Asia (regions 13/14/15). The following attributes are provided in the table: RGIID: unique identifier from the RGI6.0 VALID: flag to indicate if the data quality of the inputs and results was acceptable (see study Supplementary Material) CenLat: Latitude of glacier outline centroid from the RGI6.0 CenLon: Longitude of glacier outline centroid from the RGI6.0 meanSMB: Glacier mean mass balance (m w.e./year) derived by this study ELA: Equilibrium Line Altitude (m a.s.l.) estimated by this study ELAsig: Uncertainty of ELA based on 1000 Monte Carlo simulations using the derived surface mass balance uncertainty AAR: Accumulation Area Ration (unitless) estimated by this study AARsig: Uncertainty of AAR as for ELA totAbl: Volume of annual ablation, glacier-wide (m3/year) totAblsig: Uncertainty of total ablation, glacier-wide (m3/year) balAbl: Volume of 'balance' annual ablation, ie that compensated by net annual accumulation, glacier-wide (m3/year) balAblsig: Uncertainty of 'balanced' ablation, glacier-wide (m3/year) imbalAbl: Volume of 'imbalance' annual ablation, glacier-wide (m3/year) imbalAblsig: Uncertainty of imbalance ablation, glacier-wide (m3/year) balAblPct: Portion of annual ablation balanced by accumulation (unitless) balAblPctsig: Uncertainty of balance portion of ablation (unitless) Vol2100: Simulated glacier volume in the year 2100 under repeated application of current mass balace (m3) Vol2100sig: Uncertainty in Vol2100 based on current mass balance uncertainty (m3) PctVol2100: Simulated volume at 2100 expressed as a fraction of volume at 2000 (unitless) PctVol2100sig: Uncertainty in PctVol2100 based on current mass balance uncertainty (unitless) Also contains 1 .zip file with principal regridded inputs and results for continuity-derived glacier specific mass balances of High Mountain Asia, 2000-2016. A subdirectory contains the following for each glacier, identified by its Randolph Glacier Inventory identification number (RGIID), all in geotiff format and at the same resolution: '*_AW3D.tif': regridded digital elevation model based on the ASTER GDEM3 (apologies for misleading name) '*_debris.tif': binary rasterized debris-cover map based on the results of Scherler et al (2018) '*_dH.tif': regridded elevation change rate from Brun et al (2017), in m per year '*_dHe.tif': regridded elevation change rate uncertainty from Brun et al (2017), in m per year '*_FDIV.tif': raster of flux divergence, in m per year '*_FDIVe.tif': raster of flux divergence uncertainty, in m per year '*_Hdensity.tif': raster of estimated density of dH signal, in 1000 kg per m3 '*_SMB.tif': raster of specific mass balance, in m w.e. per year '*_SMBe.tif': raster of specific mass balance uncertainty, in m w.e. per year '*_Smean.tif': raster of column-average surface speed based on regridded data from ITS_LIVE (Gardner et al, 2019), in m per year '*_THX.tif': raster of glacier thickness from consensus estimate of Farinotti et al (2019), in m '*_zFDIV.tif': raster of zonally-aggregated flux divergence, in m per year '*_zFDIVe.tif': raster of zonally-aggregated flux divergence uncertainty, in m per year '*_zones.tif': raster of elevation-based zonal segmentation for each glacier '*_zSMB.tif': raster of zonally-aggregated specific mass balance, in m w.e. per year '*_zSMBe.tif': raster of zonally-aggregated specific mass balance uncertainty, in m w.e. per year

This dataset is no longer being updated as of 5/11/2023. It is being retained on the Open Data Portal for its potential historical interest.

This dataset shows positive COVID-19 Cases in Cambridge by neighborhood. It is reported to Cambridge by the Commonwealth of Massachusetts once per day. Of Note:

Population data are from Cambridge Community Development, and are sourced from the 2013-2017 American Community Survey estimates, and may differ from actual population counts.

Cases for which the home address is missing, misspelled, or incorrect (i.e., not an actual Cambridge address) may not be represented on the maps. For these reasons, the total case count reflected in the maps is lower than the current case count for the city.

The maps reflect the time period of March 10, 2020 (first known positive case) through present. Cases are not removed from the maps when a resident recovers or passes away. The maps do not include COVID-19 cases among Cambridge residents in skilled nursing and assisted living facilities.

Data are updated once per day. Case counts are subject to change.

The Cambridge Public Health Department (CPHD) is using a tool called “geocoder,” developed by the City’s Information Technology Department, to assign the home addresses of cases to one of the city’s 13 neighborhoods. The geocoder tries to match each case address to the City's official address list. Cambridge's geocoder is run locally and off-network to ensure health data privacy.

To learn more about the demographics of the city’s neighborhoods, see City of Cambridge Neighborhood Statistical Profile 2019.

Terms of Use:

Data Limitations Disclaimer

The MassDEP Estimated Sewer System Service Area Boundaries datalayer may not be complete, may contain errors, omissions, and other inaccuracies, and the data are subject to change. The user’s use of and/or reliance on the information contained in the Document (e.g. data) shall be at the user’s own risk and expense. MassDEP disclaims any responsibility for any loss or harm that may result to the user of this data or to any other person due to the user’s use of the Document.

All sewer service area delineations are estimates for broad planning purposes and should only be used as a guide. The data is not appropriate for site-specific or parcel-specific analysis. Not all properties within a sewer service area are necessarily served by the system, and some properties outside the mapped service areas could be served by the wastewater utility – please contact the relevant wastewater system. Not all service areas have been confirmed by the sewer system authorities.

This is an ongoing data development project. Attempts have been made to contact all sewer/wastewater systems, but not all have responded with information on their service area. MassDEP will continue to collect and verify this information. Some sewer service areas included in this datalayer have not been verified by the POTWs, privately-owned treatment works, GWDPs, or the municipality involved, but since many of those areas are based on information published online by the municipality, the utility, or in a publicly available report, they are included in the estimated sewer service area datalayer.

Please use the following citation to reference these data

MassDEP. Water Utility Resilience Program. 2025. Publicly-Owned Treatment Work and Non-Publicly-Owned Sewer Service Areas (PubV2024_12).

We want to learn about the data uses. If you use this dataset, please notify staff in the Water Resilience program (WURP@mass.gov).

Layers and Tables:

The MassDEP Estimated Sewer System Service Area data layer comprises two feature classes and a supporting table:

Publicly-Owned Treatment Works (POTW) Sewer Service Areas feature class SEWER_SERVICE_AREA_POTW_POLY includes polygon features for sewer service areas systems operated by publicly owned treatment works (POTWs)Non-Publicly Owned Treatment Works (NON-POTW) Sewer Service Areas feature class SEWER_SERVICE_AREA_NONPOTW_POLY includes polygon features for sewer service areas for operated by NON publicly owned treatment works (NON-POTWs)The Sewer Service Areas Unlocated Sites table SEWER_SERVICE_AREA_USL contains a list of known, unmapped active POTW and NON-POTW services areas at the time of publication.

ProductionData Universe

Effluent wastewater treatment plants in Massachusetts are permitted either through the Environmental Protection Agency’s (EPA) National Pollutant Discharge Elimination System (NPDES) surface water discharge permit program or the MassDEP Groundwater Discharge Permit Program. The WURP has delineated active service areas served by publicly and privately-owned effluent treatment works with a NPDES permit or a groundwater discharge permit.

National Pollutant Discharge Elimination System (NPDES) Permits

In the Commonwealth of Massachusetts, the EPA is the permitting authority for regulating point sources that discharge pollutants to surface waters. NPDES permits regulate wastewater discharge by limiting the quantities of pollutants to be discharged and imposing monitoring requirements and other conditions. NPDES permits are typically co-issued by EPA and the MassDEP. The limits and/or requirements in the permit ensure compliance with the Massachusetts Surface Water Quality Standards and Federal Regulations to protect public health and the aquatic environment. Areas served by effluent treatment plants with an active NPDES permit are included in this datalayer based on a master list developed by MassDEP using information sourced from the EPA’s Integrated Compliance Information System (ICIS).

Groundwater Discharge (GWD) Permits

In addition to surface water permittees, the WURP has delineated all active systems served by publicly and privately owned effluent treatment works with groundwater discharge (GWD) permits, and some inactive service areas. Groundwater discharge permits are required for systems discharging over 10,000 GPD sanitary wastewater – these include effluent treatment systems for public, district, or privately owned effluent treatment systems. Areas served by an effluent treatment plant with an active GWD permit are included in this datalayer based on lists received from MassDEP Wastewater staff.

Creation of Unique IDs for Each Service Area

The Sewer Service Area datalayer contains polygons that represent the service area of a particular wastewater system within a particular municipality. Every discharge permittee is assigned a unique NPDES permit number by EPA or a unique GWD permit identifier by MassDEP. MassDEP WURP creates a unique Sewer_ID for each service area by combining the municipal name of the municipality served with the permit number (NPDES or GWD) ascribed to the sewer that is serving that area. Some municipalities contain more than one sewer system, but each sewer system has a unique Sewer_ID. Occasionally the area served by a sewer system will overlap another town by a small amount – these small areas are generally not given a unique ID. The Estimated sewer Service Area datalayer, therefore, contains polygons with a unique Sewer_ID for each sewer service area. In addition, some municipalities will have multiple service areas being served by the same treatment plant – the Sewer_ID for these will contain additional identification, such as the name of the system, to uniquely identify each system.

Classifying System Service Areas

WURP staff reviewed the service areas for each system and, based on OWNER_TYPE, classified as either a publicly-owned treatment work (POTW) or a NON-POTW (see FAC_TYPE field). Each service area is further classified based on the population type served (see SECTOR field).

Methodologies and Data Sources

Several methodologies were used to create service area boundaries using various sources, including data received from the sewer system in response to requests for information from the MassDEP WURP project, information on file at MassDEP, and service area maps found online at municipal and wastewater system websites. When MassDEP received sewer line data rather than generalized areas, 300-foot buffers were created around the sewer lines to denote service areas and then edited to incorporate generalizations. Some municipalities submitted parcel data or address information to be used in delineating service areas. Many of the smaller GWD permitted sewer service areas were delineated using parcel boundaries related to the address on file.

Verification Process

Small-scale pdf file maps with roads and other infrastructure were sent to systems for corrections or verifications. If the system were small, such as a condominium complex or residential school, the relevant parcels were often used as the basis for the delineated service area. In towns where 97% or more of their population is served by the wastewater system and no other service area delineation was available, the town boundary was used as the service area boundary. Some towns responded to the request for information or verification of service areas by stating that the town boundary should be used since all, or nearly all, of the municipality is served by one wastewater system.

To ensure active systems are mapped, WURP staff developed two work flows. For NPDES-permitted systems, WURP staff reviewed available information on EPA’s ICIS database and created a master list of these systems. Staff will work to routinely update this master list by reviewing the ICIS database for new NPDES permits. The master list will serve as a method for identifying active systems, inactive systems, and unmapped systems. For GWD permittees, GIS staff established a direct linkage to the groundwater database, which allows for populating information into data fields and identifying active systems, inactive systems, and unmapped systems.

All unmapped systems are added to the Sewer Service Area Unlocated List (SEWER_SERVICE_AREAS_USL) for future mapping. Some service areas have not been mapped but their general location is represented by a small circle which serves as a placeholder - the location of these circles are estimated based on the general location of the treatment plant or the general estimated location of the service area - these do not represent the actual service area.

Percent Served Statistics The attribute table for the POTW sewer service areas (SEWER_SERVICE_AREA_POTW_POLY) has several fields relating to the percent of the town served by the particular system and one field describing the percent of town served by all systems in the town. The field ‘Percent AREA Served by System’ is strictly a calculation done dividing the area of the system by the total area of the town and multiplying by 100. In contrast, the field ‘Percent Served by System’, is not based on a particular calculation or source – it is an estimate based on various sources – these estimates are for planning purposes only. Data includes information from municipal websites and associated plans, the 1990 Municipal Priority list from CMR 310 14.17, the 2004 Pioneer Institute for Public Policy Research “percent on sewer” document, information contained on NPDES Permits and MassDEP Wastewater program staff input. Not all POTW systems have percent served statistics. Percentage may reflect the percentage of parcels served, the percent of area within a community served or the population served and should not be used for legal boundary definition or regulatory interpretation.

Sources of information for estimated wastewater service areas:

EEOA Water Assets

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

Morocco MA: Risk of Catastrophic Expenditure for Surgical Care: % of People at Risk data was reported at 27.300 % in 2017. This records a decrease from the previous number of 28.300 % for 2016. Morocco MA: Risk of Catastrophic Expenditure for Surgical Care: % of People at Risk data is updated yearly, averaging 34.300 % from Dec 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 43.700 % in 2004 and a record low of 27.300 % in 2017. Morocco MA: Risk of Catastrophic Expenditure for Surgical Care: % of People at Risk 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: Health Statistics. The proportion of population at risk of catastrophic expenditure when surgical care is required. Catastrophic expenditure is defined as direct out of pocket payments for surgical and anaesthesia care exceeding 10% of total income.; ; The Program in Global Surgery and Social Change (PGSSC) at Harvard Medical School (https://www.pgssc.org/); Weighted average;

Linear regression of foreclosures on body mass index (BMI) with individual fixed effects for Medicaid Patients.

According to our current knowledge, there is insufficient data or information available regarding the effects of the COVID-19 lockdown on BMI, eating habits, and physical activity levels among the population of Bangladesh, including university students. Nevertheless, it is crucial to promptly address the consequences of the COVID-19 lockdown, such as the loss of educational hours, learning setbacks, mental stress, and physical disruptions. The existing data highlights perceived changes in BMI, eating habits, and physical activity levels among university students in Bangladesh before and during the COVID-19 lockdown. Hence, policymakers or governments can utilize this dataset in various ways to implement effective and targeted measures to alleviate the impacts caused by the prolonged social lockdown on students.