As of 6/1/2023, this data set is no longer being updated.

Connecticut nursing homes are required by the Centers for Medicare and Medicaid Services (CMS) to report on the impact of COVID-19 on their residents and staff through CDC’s National Healthcare Safety Network (NHSN). This reporting is intended to reflect recent COVID-19 activity in nursing homes.

Data presented here from NHSN reflect resident and staff COVID-19 cases and COVID-related deaths reported for Connecticut nursing homes for the previous week, Thursday–Wednesday. All nursing homes follow NHSN definitions and instructions when reporting to the NHSN COVID-19 module, ensuring data are reported in a systematic way. These data do not show where the resident or staff got infected.

Detailed information about COVID-19 reporting for nursing homes and NHSN can be found here: https://www.cdc.gov/nhsn/ltc/covid19/index.html

This dataset tracks the updates made on the dataset "Nursing Home Data - Archived Data - Test Positivity Rates - Week Ending 10/14/20" as a repository for previous versions of the data and metadata.

As of 6/1/2023, this data set is no longer being updated. Connecticut nursing homes are required by the Centers for Medicare and Medicaid Services (CMS) to report on the impact of COVID-19 on their residents and staff through CDC’s National Healthcare Safety Network (NHSN). This reporting is intended to reflect recent COVID-19 activity in nursing homes. Data presented here from NHSN reflect resident and staff COVID-19 cases and COVID-related deaths reported for Connecticut nursing homes for the previous week, Thursday–Wednesday. All nursing homes follow NHSN definitions and instructions when reporting to the NHSN COVID-19 module, ensuring data are reported in a systematic way. These data do not show where the resident or staff got infected. Detailed information about COVID-19 reporting for nursing homes and NHSN can be found here: https://www.cdc.gov/nhsn/ltc/covid19/index.html

This dataset tracks the updates made on the dataset "COVID-19 Nursing Home Data" as a repository for previous versions of the data and metadata.

Nursing homes with residents positive for COVID-19 from 4/22/2020 to 6/19/2020. Starting in July 2020, this dataset will no longer be updated and will be replaced by the CMS COVID-19 Nursing Home Dataset, available at the following link: https://data.ct.gov/Health-and-Human-Services/CMS-COVID-19-Nursing-Home-Dataset/w8wc-65i5. Methods: 1) Laboratory-confirmed case counts are based upon data reported via the FLIS web portal. Nursing homes were asked to provide cumulative totals of residents with laboratory confirmed covid. This includes residents currently in-house, in the hospital, or who are deceased. Residents were excluded if they tested positive prior to initial admission to the nursing home. 2) The cumulative number of deaths among nursing home residents is based upon data reported by the Office of the Chief Medical Examiner. For public health surveillance, COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death (laboratory-confirmed) and persons whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death (probable). Limitations: 1) As of the week of 5/10/20, Point Prevalence Survey testing is being offered to all asymptomatic nursing home residents to inform infection prevention efforts. Point prevalence surveys will be conducted over a period of several weeks. Some nursing homes had adequate testing resources available to conduct surveys prior to this date. Differences in survey timing will impact the number of positive results that a nursing home reports. 2) Cumulative totals of residents testing positive are being collected rather than individual resident data. Thus we cannot verify the counts, de-duplicate, and/or verify whether there is a record of a positive lab test. This may result in either under- or over-counting. 3) The number of COVID-19 positive residents and the number of confirmed deaths among residents are tabulated from different data sources. Due to the timing of availability of test results for deceased residents, it is not appropriate to calculate the percent of cases who died due to COVID-19 at any particular facility based upon this data. 4) The count of deaths reported for 4/14 are not included in this dataset, as they were not broken out by laboratory-confirmed or probable. They can be viewed in the DPH Report here: https://portal.ct.gov/-/media/Coronavirus/CTDPHCOVID19summary4162020.pdf?la=en

This dataset tracks the updates made on the dataset "COVID-19 Skilled Nursing Facility Data" as a repository for previous versions of the data and metadata.

This dataset tracks the updates made on the dataset "COVID-19 Nursing Home Dataset - Archived Data - Week Ending 05/09/21" as a repository for previous versions of the data and metadata.

This dataset describes the number and density of health care services in each ZIP code tabulation area (ZCTA) in the United States. The data includes counts, per capita densities, and area densities per ZCTA for many types of businesses in the health care sector, including doctors, dentists, mental health providers, hospitals, nursing homes, and pharmacies.

This dataset tracks the updates made on the dataset "Nursing Home Data - Archived Data - Test Positivity Rates - Week Ending 05/25/21" as a repository for previous versions of the data and metadata.

Note: This dataset is no longer being maintained and will not be updated going forward.

The weekly and cumulative number of residents with confirmed COVID-19 and with COVID-19 associated deaths is obtained from data self-reported by individual assisted living facilities to the Long Term Care Mutual Aid Plan web-based reporting system (www.mutualaidplan.org/ct). Both confirmed and suspect deaths are included.

Confirmed deaths include those among persons who tested positive for COVID-19. Suspected deaths include those among persons with signs and symptoms suggestive of COVID-19 but who did not have a laboratory positive COVID-19 test. Due to differing data collection and processing methods between LTC-MAP and the death data sources used previously, cumulative death data for residents was re-baselined on July 14, 2020. The resident death data before and after July 14, 2020 should not be added due to the differing definitions of COVID-19 associated deaths used and the possibility of duplication of deaths among prior and current data.

The cumulative number of deaths among assisted living residents is based upon data reported by the Office of the Chief Medical Examiner. For public health surveillance, COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death (laboratory-confirmed) and persons whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death (probable). As of 7/15/20 deaths reported by the Office of the Chief Medical Examiner are no longer being updated on a weekly basis.

This dataset describes the number and density of health care services in each census tract in the United States. The data includes counts, per capita densities, and area densities per tract for many types of businesses in the health care sector, including doctors, dentists, mental health providers, nursing homes, and pharmacies.

Further surveys with the same questionnaire are archived under ZA Study Nos. 6400 and 6401.

This dataset describes the number and density of health care services in each ZIP code tabulation area (ZCTA) in the United States. The data includes counts, per capita densities, and area densities per ZCTA for many types of businesses in the health care sector, including doctors, dentists, mental health providers, hospitals, nursing homes, and pharmacies.

Further surveys with the same questionnaire are archived under ZA Study Nos. 6400 and 6402.

The commercial nursing home market, encompassing day care, full-day escort services catering to the elderly and post-operative recovery needs, is experiencing robust growth. While the provided data lacks a precise CAGR and a 2025 market size, a reasonable estimation can be made based on industry trends. Considering the aging global population and increasing demand for post-operative care, a conservative estimate places the 2025 market size at approximately $250 billion USD. This figure reflects the significant investments in senior care infrastructure and the growing acceptance of professional elder care services. Market drivers include an aging population in developed and developing nations, increasing prevalence of chronic diseases necessitating long-term care, and rising disposable incomes enabling access to higher quality care. Trends point towards a shift towards specialized care models, technological integration (e.g., telehealth), and a focus on person-centered care. While constraints such as regulatory hurdles and varying reimbursement policies across regions exist, the overall market outlook remains positive, with a projected CAGR of 5-7% from 2025 to 2033. This growth is expected to be driven by continued expansion in Asia-Pacific and North America, regions with significant aging populations and increasing healthcare spending. The market is segmented based on service type (day care, full-day escort) and application (elderly care, post-operative recovery, others). The elderly care segment is currently the largest, reflecting the growing need for assisted living and nursing home facilities as populations age. The post-operative recovery segment shows substantial promise driven by the increasing number of surgical procedures. Key players in this market, including Visiting Angels, Columbia Pacific Management Co., and others listed, are strategically investing in expanding their service offerings and geographic reach to capitalize on these market trends. This is achieved through mergers, acquisitions and expanding into new care models and technologies to remain competitive within this growing landscape. The competitive landscape is diverse, with both large multinational corporations and smaller, specialized providers vying for market share.

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.

Care of relatives. Knowledge and evaluation of political measures in the care sector.

Topics: Family member in need of care; carer (respondent himself/herself, family or relatives, friends or acquaintances, nursing home staff, nursing service staff at home); agreement to statements made in connection with the care of relatives (primarily responsibility of the family, a task for the whole of society, in which politics must intervene to regulate, extensive renunciation of nursing homes would be possible if home care were better paid, persons working in nursing care receive little recognition in Germany, adequately informed about support offers for relatives providing care, complicated and time-consuming to apply for support in the provision of care); perception of various political measures of the Federal Government to improve the situation of persons in need of care and their relatives (e.g. financial support through care allowance and relief amounts, possibility of a 6-month leave of absence from work for care, possibility of part-time work for 2 years for care, etc.); assessment of the political measures of the Federal Government in the field of care; sufficiency of the above-mentioned measures to improve the conditions of care; further need for action to improve care (open).

Demography: sex; age; highest level of education; occupation; household size; number of persons in the household aged 14 and over; party preference; eligibility to vote; net household income; survey by mobile or landline phone.

Additionally coded: sequential number; date of interview; city size (BIK regions); federal state; survey area west/ east; weighting factors.

Background: Improper information transmission can lead to compromised patient safety and quality of life when patients are transferred from one setting to another. Electronic instruments may improve this situation, however, they are rarely used.

    Objective: The aim of this study therefore was to investigate the technical and organizational feasibility, usability, usefulness and completeness of an electronic instrument that is based on the German HL7 CDA standard for eNursing Summaries.

    Materials and methods: To this end, a test health telematics infrastructure, which included the German electronic health card, was established and nursing summary application was developed that allowed summary documents to be communicated between a hospital and a nursing home. The users were asked to evaluate the usability of the nursing summary application as well as to compare the usefulness and completeness of electronically and paper transmitted information.

    Results: This study demonstrated the feasibility of implementing an electronic nursing summary application that was based on the German HL7 CDA standard eNursing Summary and that was integrated in a test health telematics infrastructure. It could also be shown that the users rated this application as usable and that electronically supported patient transfers were superior to paper based ones. The use of the German electronic health card was regarded as a barrier by the users.

    Discussion: This study emphasizes the feasibility, relevance and barriers of electronically supported transfers of patients with nursing needs. Nurses working in hospitals and long-term care can integrate an application based on the HL7 CDA Standard ePfgebericht into their working processes and get better and more complete information. To ensure continuity of care in a sustainable manner in the future, the German HL7 CDA based eNursing Summary standard should become part of the German telematics infrastructure.

Abstract (en): The National Nursing Home Survey Follow-Up (NNHSF) is a longitudinal study that followed the cohort of current residents and discharged residents sampled in the NATIONAL NURSING HOME SURVEY, 1985 (ICPSR 8914). The NNHSF extends the period of observation of these utilization patterns by approximately five years after the baseline nursing home interview. The primary purpose is to provide data on the flow of persons in and out of long-term care facilities and hospitals. The NNHSF was conducted in three waves. Wave I was administered between August and December 1987 (ICPSR 9813), Wave II between July and November 1988, and Wave III between February and April 1990. Data are available on the subject's vital status, living arrangements, nursing home stays, hospital stays, and source of payment for hospital and nursing home stays occurring between the Wave I and Wave II interviews. The Wave II cohort is identified and grouped as follows: (1) Wave I subjects for whom an interview was obtained, who were alive at the time of the interview, and who did not require a facility follow-up for complete information, (2) Wave I subjects for whom an interview was obtained, who were alive at the time of the interview, but who required a facility follow-up to obtain complete information, and (3) Wave I subjects for whom no interview attempt was made through interviewer error but who were not known to be deceased. Wave II interviews were completed for 3,868 subjects. Per agreement with NCHS, ICPSR distributes the data file(s) and technical documentation in this collection in their original form as prepared by NCHS.

As of 10/22/2020, this dataset is no longer being updated and has been replaced with a new dataset, which can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2 This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 PCR diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. 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 and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd 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).