In 2023, unpaid family caregivers in the United States could use some help from certain services and support. Three quarters of present and past caregivers said in-home services by paid professionals would be helpful. All the services and support listed were wanted by over half of the surveyed caregivers, including adult day care and social opportunities. Family caregivers are often physically and emotionally stressed and could use a break from their caregiving responsibilities, known as respite care. Respite care can be offered in the home, in institutions, or in the community, such as an adult day care center.

The Older Persons and Informal Caregivers Survey - Minimum DataSet (TOPICS-MDS) is a public data repository which contains information on the physical and mental health and well-being of older persons and informal caregivers and their care use across the Netherlands. The database was developed at the start of The National Care for the Elderly Programme (‘Nationaal Programma Ouderenzorg’ - NPO) on behalf of the Organisation of Health Research and Development (ZonMw - The Netherlands), in part to ensure uniform collection of outcome measures, thus promoting comparability between studies.Between 2008 en 2016, 53 different research projects have contributed data to this initiative, resulting in a pooled dataset with cross-sectional and (partly) longitudinal data of >43,000 older persons and >9,000 informal caregivers. Out of these numbers, a number of 7,600 concerns care receiver - caregiver dyads of whom information on both the care receiver and caregiver is available.The 'TOPICS-MDS NPO caregiver’ dataset contains no care receiver (older person) data, only informal caregiver data.

The Older Persons and Informal Caregivers Survey - Minimum DataSet (TOPICS-MDS) is a public data repository which contains information on the physical and mental health and well-being of older persons and informal caregivers and their care use across the Netherlands. The database was developed at the start of The National Care for the Elderly Programme (‘Nationaal Programma Ouderenzorg’ - NPO) on behalf of the Organisation of Health Research and Development (ZonMw - The Netherlands), in part to ensure uniform collection of outcome measures, thus promoting comparability between studies.Since September 2014, TOPICS-MDS data are also collected within the ZonMw funded ‘Memorabel’ programme, that is specifically aimed at improving the quality of life for people with dementia and the care and support provided to them. In Memorabel round 1 through 4, 11 different research projects have collected TOPICS-MDS data, which has resulted in a pooled database with cross-sectional and (partly) longitudinal data of 1,400 older persons with early onset or advanced dementia and about 950 informal caregivers. Out of these numbers, a number of 919 concerns care receiver - caregiver dyads of whom information on both the care receiver and caregiver is available.More background information on both NPO and Memorabel 1-4 can be found in the overall information on TOPICS-MDS under the tab ‘Data files’ in DANS EASY (doi.org/10.17026/dans-xvh-dbbf).At the moment, 3 different research projects have collected data for TOPICS-MDS Memorabel 5.The 'TOPICS-MDS Memorabel 5 caregiver dataset, as part of the Memorabel 5 database, contains only data about the caregiver, such as data on physical and emotional health, time spent on care receiver and quality of life. Date Submitted: 2023-10-05

The Older Persons and Informal Caregivers Survey - Minimum DataSet (TOPICS-MDS) is a public data repository which contains information on the physical and mental health and well-being of older persons and informal caregivers and their care use across the Netherlands. The database was developed at the start of The National Care for the Elderly Programme (‘Nationaal Programma Ouderenzorg’ - NPO) on behalf of the Organisation of Health Research and Development (ZonMw - The Netherlands), in part to ensure uniform collection of outcome measures, thus promoting comparability between studies.Since September 2014, TOPICS-MDS data are also collected within the ZonMw funded ‘Memorabel’ programme, that is specifically aimed at improving the quality of life for people with dementia and the care and support provided to them. In Memorabel round 1 through 4, 11 different research projects have collected TOPICS-MDS data, which has resulted in a pooled database with cross-sectional and (partly) longitudinal data of 1,400 older persons with early onset or advanced dementia and about 950 informal caregivers. Out of these numbers, a number of 919 concerns care receiver - caregiver dyads of whom information on both the care receiver and caregiver is available.More background information on both NPO and Memorabel 1-4 can be found in the overall information on TOPICS-MDS under the tab ‘Data files’ in DANS EASY (doi.org/10.17026/dans-xvh-dbbf).At the moment, 3 different research projects have collected data for TOPICS-MDS Memorabel 7.The 'TOPICS-MDS Memorabel 7 care receiver' dataset, as part of the Memorabel 5 database, contains no informal caregiver data, only care receiver (older person) data. The dataset includes data on age, gender, country of birth, level of education, marital status and living situation of the care receiver, as well as data on physical and emotional health and well-being, quality of life, daily functioning and use of care, such as GP visits, home care, day care/treatment and admittance in a hospital, home for the aged or nursing home. Date Submitted: 2023-10-05

This indicator includes children whose caregivers reported it is somewhat or very difficult to get childcare on a regular basis when needed. Children who attend a kindergarten and children who do not need childcare were excluded from data analyses.Access to high-quality, affordable childcare can help provide employment stability and reduce financial hardship and parental stress. Quality childcare has also been shown to have both short- and long-term positive effects on children’s cognitive, biological, and social development.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

The project aimed to understand whether young adults who take care of a loved-one (young adult caregivers; YACs) differ in their perceived life balance and psychosocial functioning as compared to young adults without care responsibilities (non-YACs). In addition, this project aimed to understand how YACs evaluated a tool to support informal careg

ivers. This tool (“Caregiver Balance”; https://balans.mantelzorg.nl) is specifically designed to support informal caregivers taking care of a loved-one in the palliative phase and could potentially be adapted to meet the needs of YACs.

In this project, we collected data of 74 YACs and 246 non-YACs. Both groups completed questionnaires, and the YACs engaged in a usability test. The questionnaire data was used to compare the perceived life balance and psychological functioning between YACs and non-YACs, aged 18-25 years, and studying in the Netherlands (study 1). Furthermore, we examined the relationship between positive aspects of caregiving and relational factors, in particular, relationship quality and collaborative coping among YACs (study 2). Finally, we conducted a usability study where we interviewed YACs to understand the needs and preferences towards a supportive web-based solution (study 3).

Table: Study details and associated files

    Number
    Study Name
    Study Aim
    Study Type
    Type of data
    Associated Files


    1
    Perceived life balance among young adult students: a comparison between caregivers and non-caregivers
    Compare the perceived life balance and psychological functions among student young adult caregivers aged 18-25 years (YACs) with young adult without care responsibilities
    Survey study
    Quantitative

ENTWINE_YACs_nonYACsSurvey_RawData

ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData

ENTWINE_ PerceivedLifeBalanceSurvey _Syntax

ENTWINE_YACs_nonYACsSurvey_codebook

    2
    Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers
    Examine the relationship of positive aspects of caregiving with relational factors, in particular, relationship quality and collaborative coping among a particular group of ICGs, young adult caregivers (YACs), aged 18-25 years.
    Survey study
    Quantitative

ENTWINE_YACs_nonYACsSurvey_RawData

ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData

ENTWINE_PositiveAspectsCaregiving_Survey_Syntax

ENTWINE_YACs_nonYACsSurvey_codebook

    3
    Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool
    Explore (i) challenges and support needs of YACs in caregiving, (ii) their needs towards the content of the ‘MantelzorgBalans’ tool, and (iii) issues they encountered in using the tool and their preferences for adaptation of the tool.
    Usability study

Qualitative and Quantitative

ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData [to be determined whether data can be shared]

ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData

Description of the files to be uploaded

Study 1: Perceived life balance among young adult students: a comparison between caregivers and non-caregivers

ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw, pseudonomyzed survey data. The following cleaned dataset ‘ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData’ was generated from this raw data.

ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData: SPSS file with the cleaned dataset having the following metadata -

Population: young adult caregivers and young adult non-caregivers aged 18-25 years studying in the Netherlands;

Number of participants: 320 participants in total (74 young adult caregivers and 246 young adult non-caregivers)

Time point of measurement: Data was collected from December 2020 till March 2022

Type of data: quantitative

Measurements included, topics covered: perceived life balance (based on the Occupational balance questionnaire [1]), burnout (Burnout Assessment Tool [2]), negative and positive affect (Positive and Negative Affect Schedule [3]), and life satisfaction (Satisfaction with Life Scale [4])

Short procedure conducted to receive data: online survey on Qualtrics platform

SPSS syntax file ‘ENTWINE_ PerceivedLifeBalanceSurvey _Syntax’ was used to clean and analyse ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset

ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset

Study 2: Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers

ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw survey data. The following cleaned dataset ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ was generated from this raw data.

ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData: SPSS file with the cleaned dataset having the following metadata -

Population: young adult caregivers aged 18-25 years studying in the Netherlands;

Number of participants: 74 young adult caregivers

Time point of measurement: Data was collected from December 2020 till March 2022

Type of data: quantitative

Measurements included, topics covered: positive aspects of caregiving (positive aspects of caregiving scale [5]), relationship quality (Relationship Assessment Scale [6]), collaborative coping (Perception of Collaboration Questionnaire [7] )

Short procedure conducted to receive data: online survey on Qualtrics platform.

SPSS syntax file ‘ENTWINE_PositiveAspectsCaregiving_Survey_Syntax’ was used to clean and analyse ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ dataset.

ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData dataset.

Study 3: Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool

ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData: Pseudonymized word file including 13 transcripts having the qualitative data from interview and usability testing with the following metadata –

Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total

Time point of measurement: data was collected from October 2021 till February 2022

Type of data: qualitative and quantitative

Measurements included, topics covered: Caregiving challenges, support needs and barriers, usability needs, preferences and issues towards eHealth tool

Short procedure conducted to receive data: Online interviews

ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData: Excel sheet having the quantitative questionnaire raw data with the following metadata

Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total

Time point of measurement: data was collected from October 2021 till February 2022

Type of data: qualitative and quantitative

Measurements included, topics covered: User experience (user experience questionnaire [8]), satisfaction of using the web-based tool (After scenario questionnaire [9]), Intention of use and persuasive potential of the eHealth tool (persuasive potential questionnaire [10])

Short procedure conducted to receive data: Online questionnaire

Data collection details

All data was collected, processed, and archived in accordance with the General Data Protection Regulation (GDPR) and the FAIR (Findable, Accessible, Interoperable, Reusable) principles under the supervision of the Principal Investigator.

The principal researcher and a team of experts (supervisors) in the field of health psychology and eHealth (University of Twente, The Netherlands) reviewed the scientific quality of the research. The studies were piloted and tested before starting the collection of the data. For the survey study, the researchers monitored the data collection weekly to ensure it was running smoothly.

The ethical review board, Centrale Ethische Toetsingscommissie of the University Medical Center Groningen, The Netherlands (CTc), granted approval for this research (Registration number: 202000623).

Participants digitally signed informed consent for participating in the study.

Terms of use

Interested persons can send a data request by contacting the principal investigator (Prof. dr. Mariët Hagedoorn, University Medical Center Groningen, the Netherlands mariet.hageboorn@umcg.nl).

Interested persons must provide the research plan (including the research question, methodology, and analysis plan) when requesting for the data.

The principal investigator reviews the research plan on its quality and fit with the data and informs the interested person(s).

(Pseudo)anonymous data of those participants who agreed on the reuse of their data is available on request for 15 years from the time of completion of the PhD project.

Data will be available in Excel or SPSS format alongside the variable codebook after the completion of this PhD project and publication of the study results.

References

1. Wagman P, Håkansson C. Introducing the Occupational Balance Questionnaire (OBQ). Scand J Occup Ther 2014;21(3):227–231. PMID:24649971

2. Schaufeli WB, Desart S, De Witte H. Burnout assessment tool (Bat)—development, validity, and reliability. Int J Environ Res Public Health 2020;17(24):1–21. PMID:33352940

3. Watson D, Clark LA, Tellegen A. Development and Validation of Brief Measures of Positive and Negative Affect: The

The national general nursing home nursing personnel and caregiver statistics form, main fields: county (city), male nursing personnel 21 to 40 years old, male nursing personnel 41 to 60 years old, male nursing personnel 61 to 70 years old, female nursing personnel 21 to 40 years old, female nursing personnel 41 to 60 years old, female nursing personnel 61 to 70 years old, male caregiver, female caregiver.

In 2021, there were ** million family caregivers across the United States. That year, California registered the country's highest number of family caregivers, followed by Texas, Florida, and New York. On the other hand, with an estimated ****** people, Wyoming registered the lowest number of family caregivers in the U.S. in 2021.

As society ages, the caregiving role and the mental health challenges among caregivers are gaining prominence. In Taiwan, long-term care nursing assistants (LTC-NAs) play a crucial role in the field of long-term care (LTC). However, some LTC-NAs engage in caregiving both at work and within their families. This study aims to investigate the association of LTC-NAs' dual caregiving roles with mental health and to determine whether social support functions as a moderating factor in this association.

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). Data for this indicator are based on caregiver report and defined according to the Children with Special Health Care Needs (CSHCN) Screening Tool developed through the Child and Adolescent Health Measurement Initiative led by The Foundation for Accountability. The CSHCN screener includes three "definitional domains." These are: (1) dependency on prescription medications; (2) service use above that considered usual or routine; and (3) functional limitations.Children with special healthcare needs and their families or caregivers may be at increased risk for economic hardship due to higher healthcare costs and more time needed off from work or school to attend medical appointments. In particular, children who have a disability may also be at increased risk for experiencing lower levels of social and emotional wellbeing compared to their peers who do not have a disability.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

The READ ME file describes the data source and analysis file for producing the tables and figures in Supporting young children with disabilities during the COVID19 pandemic: Evidence from caregivers in VirginiaAbstract:Early care and education (ECE) experiences shape children’s developmental trajectories, particularly for children who have or may have disabilities. However, caregivers of children with disabilities have faced considerable challenges finding care for their children, which have increased during the COVID19 pandemic. Using survey data from nearly 3,000 caregivers of preschool age children in Virginia collected in December 2020-January 2021, we find that caregivers of children with disabilities were less likely to find ECE programs that met their needs, more likely to experience high-levels of stress, and more likely to be concerned about their child’s development than were caregivers of children without disabilities. Concerns about child development were particularly pronounced among caregivers of children with disabilities in remote/hybrid instructional settings. Our findings suggest a disproportionate impact of ECE disruption on caregivers of children with disabilities and the need for targeted supports for these families moving forward.

The ability to retain and remember information (memory) is essential to caregiving tasks. There is evidence that caregivers are at greater risk for experiencing deteriorations in cognitive status than non-caregivers, especially memory; however, we have a limited understanding of factors that are related to changes in caregiver memory. This scoping review intends to comprehensively map factors related to caregiver memory reported in the literature within the chronic caregiving context. Specific aims include (1) identifying factors related to caregiver memory; (2) examining how caregiver memory has been measured; and (3) describing changes in caregiver memory during their caregiving period. This review will be conducted following Arksey and O’Malley’s framework and reported using the PRISMA guidelines for Scoping Reviews (PRISMA-ScR). Studies will be included if (1) the studies focus on home-based unpaid long term family caregiving; (2) study participants (patients), of any age, have one (or more) chronic illness or disability and receive care from a caregiver for 6 months or more; (3) caregivers are adults (> = 18 years of age). Any chronic disease or condition will be included. The search will encompass gray literature and peer-reviewed literature in MEDLINE (via Ovid), CINAHL Plus with Full Text (via EBSCOhost), Embase (via Elsevier), APA PsycINFO (via EBSCOhost), Sociology Source Ultimate (via EBSCOhost), and ProQuest Dissertations and Theses Global. Data extraction will include specific details about the participants, concept, context, study methods, and key caregiver-related findings. The Caregiver Health Model will provide a framework to categorize factors that impact caregivers’ memory including caregiver health promotion activities, caregiver attitudes and beliefs, caregiver task, and caregiver needs. Factors that do not fall into the Caregiver Health Model domains will be organized by emerging themes.

Today, with the increase in the elderly population, the need for caregivers is also increasing. Although care is usually provided by family members, relatives and close environment to meet this need, institutional care is also an important service in addition to home care. The events, feelings and thoughts experienced by caregivers while giving care can positively or negatively affect their care burden, life satisfaction and sense of loneliness. The subject of the study was determined based on the questions of whether caregivers of the elderly are afraid of loneliness, whether the care burden scores of home care professionals and nursing home caregivers are the same, whether caring for the elderly decreases life satisfaction, whether the thought of spending old age with the caregiver increases the loneliness score. Our study is a team work. Mine Karaabalı, one of the team members, is responsible for the management of the project. Şevval Akıncı will be responsible for obtaining the survey data, analyzing and evaluating the data will be done by Duygu Meral, reporting the findings will be done by Zeynep Tusem Durmaz, and Alanur Yıldız will be responsible for presenting the reported findings. In the literature, there is no study on nursing home caregivers' perception of loneliness, the perception of the caregiver as a care receiver is not examined, life satisfaction and burden of care are not examined in terms of nursing home caregivers, and there are no studies in three categories: nursing home caregivers, professional caregivers at home and family caregivers. Therefore, the hypotheses of the study were elaborated according to these shortcomings. The research will be conducted in order to determine the relationship between care burden, loneliness and life satisfaction of family members who provide care in care centers, nursing homes and home care affiliated to the Ministry of Family and Social Services in Turkey. The research will be conducted between November 2023 and February 2024.The surveys to be conducted to obtain the data will be collected online and face-to-face. The data will be informed before collection and will be obtained with their consent. In the collected data, the individuals participating in the research are expected to be between the ages of 18-65, as well as not having any mental problems. A total of 357 people, including nursing home caregivers, home caregivers and professional caregivers, are expected to participate in our research. In our research, Demographic Information Form, Adult Life Satisfaction Scale, Caregiving Burden Scale and Social, Empathic Tendency Scale for Adults and Emotional Loneliness Scale will be used. The data obtained will be analyzed with SPSS 27 program. The work sections of the research are obtaining the survey data, evaluation of the results and statistical analysis, reporting the findings and presenting the project. The findings can be used to improve conditions in relevant institutions and organizations. In scientific research, it can contribute to the deepening of the researched subject and the development of the literature. It can broaden the society's perspective on the care receiver and caregiver; it can provide a new perspective. It is expected that family caregivers at home, professional caregivers at home and professional caregivers in nursing homes will be categorized and participate in the study. It can be repeated for those who want to conduct further research on the relevant topic, or the research can be redesigned with different components by taking the causes and results of the research as an example. For any questions or problems related to the project, please contact mine.karaabali@stu.fsm.edu.tr. Hypothesis: H1: There is a relationship between the mean empathic tendency scores and the mean loneliness scores of professional caregivers at home, family caregivers at home and institutional caregivers. H1A: The care burden score of professional caregivers at home is less than that of caregivers in nursing homes. H1B: There is a relationship between the mean care burden score of family caregivers at home and the mean loneliness score. H1C: The thought of spending old age in a nursing home increases the care burden score of caregivers working in nursing homes. H2: There is a statistically significant difference between the mean perceived care burden scores of home care professionals, family caregivers and institutional care professionals. H3: There is a statistically significant difference between the mean life satisfaction scores of home care professionals, family caregivers and nursing home care professionals. H4: There is a statistically significant difference between the mean loneliness scores of home care professionals, family caregivers and nursing home care professionals.

BackgroundFamily caregivers often play a key role in medical decision-making for patients with cancer. Adult-children account for nearly half of caregivers, but often have less experience with serious illness care and decision-making and face unique relational challenges as the patient’s child. Yet little research explores the potentially distinctive decision-making, involvement in decisions, and support needs of adult-child caregivers.MethodsAnalysis of survey data of U.S. cancer caregivers conducted by CancerCare® in 2021. Chi-square tests and multivariable regression models assessed whether adult-child and spousal caregivers differed on the type of medical decisions they participated in (e.g., treatment planning, medication management), who made the decision (e.g., caregiver or joint decision), and the resources that informed decisions (e.g., friends and family, education materials).ResultsAdult-children (N = 892) were less likely than spouses (N = 314) to participate in treatment planning (beta = -0.41; 95%CI = -0.81,-0.01), but more likely to be involved in decisions about whether to challenge medical authority (e.g., seeking alternative treatment, second opinion) (beta = 0.50; 95%CI = 0.22,0.78). Compared to spouses, adult-children made joint decisions with patients less often (-13.2-percentage points; 95%CI = -19.64,-6.67) and acted as primary decision-maker more frequently (5.60-percentage points; 95%CI = 0.01,10.43). More adult-children than spouses sought help and information regarding decisions from the oncology team (8.42-percentage points; 95%CI = 1.98,14.87) and friends and family (7.91-percentage points; 95%CI = 1.34,14.48).ConclusionsHow cancer caregivers and patients are related to each other shapes caregivers’ medical decision-making. Adult-children’s and spouses’ probabilities of participating in and influencing decisions differed for certain types of decisions while adult-children were more likely to seek information and social support regarding decisions. These findings highlight the importance of the patient’s and caregiver’s relationship type in medical decision-making, suggesting that decision support programs may be more effective if they tailor programs by relationship type.

Source of information: Long-term care institutions and related management information system data produced by the Ministry of Health and Welfare.Data period: From January 1, 2014 to December 31, 2014.Instructions for filling out the form:1. The number of care service personnel in the county and the whole country are calculated on a per capita basis (as the long-term care personnel were not limited to one place before September 1, 2013, the registration number is used for statistics).2. Personnel with certification documents for long-term care personnel can apply for registration, and there may be duplicate values among various professions.3. The statistical types of care service personnel institutions include: home-based, community-based, residential, comprehensive long-term care institutions established in accordance with the Long-term Care Services Act, as well as other statutory elderly welfare institutions, nursing homes, and institutions for people with disabilities (excluding C-base).4. Compiled on April 7, 2015 of the Republic of China.

Home care providers support the overall health and well-being of millions in the US annually. This number has been growing fast, expanding the scale and scope of home care providers in recent years. A rising number of adults 65 and older has been the primary driver behind this, as older adults are at a higher risk of developing a condition or experiencing an injury that limits their ability to perform tasks they once did independently. While changing demographic trends are an overarching trend impacting the health sector, the pandemic has permanently altered the industry's trajectory. Widespread outbreaks at residential facilities in the first year of the pandemic led more people to value remaining in their homes as they age; the interest in aging-in-place has only grown even as pandemic concerns have dissipated as older adults look for options that provide safety and independence. In all, revenue has been expanding at a CAGR of 3.5% to an estimated $153.7 billion over the past five years, including expected growth of 3.2% in 2025. The mounting need for home care services and a shortage of home health aides create a mismatch between supply and demand that limits revenue growth. Shortages, preexisting the pandemic, have worsened as caregivers seek more flexible jobs with higher pay, creating increasingly high turnover that pressures providers to raise wages. Medicare reimbursements to home health agencies have been declining for several years, preventing home health agencies from raising salaries despite shortages. Clients eligible for home care services through insurance face long waiting periods, leading more people to opt for self-directed care, where family members or friends work as paid caregivers. Too few caregivers prevent the industry from fully benefiting from ballooning demand and curtail profit growth. Trends driving growth in recent years will accelerate moving forward, providing massive opportunities for home care providers. How home care providers capitalize on these trends will depend on insurer reimbursements and workforce development. Technology, ranging from wearables to telehealth, will have a more prominent role in the industry as providers look for ways to improve patient care while lessening the burden on staff. Regulatory and financial pressures will maintain consolidation activity, with private equity investment likely to expand as well. A major headwind facing the industry will be the future of Medicare policies and to what extent they cover home health and telehealth services. Revenue will grow at a CAGR of 2.8% to an estimated $176.8 billion over the next five years.

The National Medical Expenditure Survey (NMES) series provides information on health expenditures by or on behalf of families and individuals, the financing of these expenditures, and each person's use of services. The Institutional Population Component (IPC) is a survey of nursing and personal care homes and facilities for the mentally retarded and residents admitted to those facilities. Information was collected on facilities and their residents at several points during 1987. Use and expenditure estimates for institutionalized persons can be combined with those from the Household Component for composite estimates covering most of the civilian population. Information on facilities and residents was collected from facility administrators and caregivers, with additional information collected from next-of-kin or other knowledgeable respondents. These data were supplemented by Medicare claims information for covered sample persons. Research File 36 provides information from the Medicare Automated Data Retrieval System (MADRS) for a subset of persons from File 1 of NATIONAL MEDICAL EXPENDITURE SURVEY, 1987: INSTITUTIONAL POPULATION COMPONENT, FACILITY USE AND EXPENDITURE DATA FOR NURSING AND PERSONAL CARE HOME RESIDENTS PUBLIC USE TAPE 17 and a subset of persons from File 1 of NATIONAL MEDICAL EXPENDITURE SURVEY, 1987: INSTITUTIONAL POPULATION COMPONENT, FACILITY USE AND EXPENDITURE DATA FOR RESIDENTS OF FACILITIES FOR PERSONS WITH MENTAL RETARDATION RESEARCH FILE 22R. Six data files are provided for Research File 36R, all of which contain demographic data such as age, sex, and race. Other variables common to all parts are facility type, person number, sample person identifier, reimbursement amount by Medicare, and total charges reported by provider. Parts 1-6 cover, respectively, Part B Payment Records, Part B Outpatient Bill Records, Part B Home Health Bill Records, Part A Inpatient/Skilled Nursing Facilities Bill Records, Part A Home Health Bill Records, and Part A Hospice Bill Records.

IntroductionDue to its increasing prevalence, dementia is currently one of the most extensively studied health issues. Although it represents a comparatively less-addressed issue, the caregiving burden for dementia patients is likewise receiving attention.MethodsTo identify determinants of depression in dementia caregivers, using Community Health Survey (CHS) data collected by the Korea Disease Control and Prevention Agency (KDCA). By setting “dementia caregiver's status of residence with patient” as a standard variable, we selected corresponding CHS data from 2011 to 2019. After refining the data, we split dementia caregiver and general population groups among the dataset (n = 15,708; common variables = 34). We then applied three machine learning algorithms: Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), and Support Vector Classifier (SVC). Subsequently, we selected XGBoost, as it exhibited superior performance to the other algorithms. On the feature importance of XGBoost, we performed a multivariate hierarchical regression analysis to validate the depression causes experienced in each group. We validated the results of the statistical model analysis by performing Welch's t-test on the main determinants exhibited within each group.ResultsBy verifying the results from machine learning via statistical model analysis, we found “sex” to highly impact depression in dementia caregivers, whereas “status of economic activities” is significantly associated with depression in the general population.DiscussionThe evident difference in causes of depression between the two groups may serve as a basis for policy development to improve the mental health of dementia caregivers.

Data set from six research sites that examined the feasibility and outcomes of the most promising home and community-based intervention approaches for enhancing family caregiving for Alzheimers Disease (AD) and related disorders (ADRD). A unique feature is the examination of AD burdens and interventions in three ethnic groups (Caucasians, Hispanics, and African Americans). Caregiver/care recipient dyads are entered into the study using standardized eligibility criteria. The dyads are randomized at each intervention site using site-specific procedures. Standardized assessment batteries are administered at baseline, 6, 12, and 18 months. The five general types of REACH interventions are: Individual Information and Support strategies that increase caregivers' understanding of dementia and their particular caregiving situation; Group Support and Family Systems efforts that provide caregivers with multiple forms of social support; Psychoeducational and Skill-Based Training approaches that teach caregivers coping and behavioral management strategies; Home-Based Environmental interventions that modify the home environment's effect on the care recipient and support the caregiver; and Enhanced Technology Systems such as home-centered computer/telephone networks that are designed to reduce caregiver distress and isolation. REACH II was funded in 2001 to test a single multi-component intervention among family caregivers of persons with ADRD, building upon the findings of REACH. Recruitment for REACH II was completed in January 2004 with 642 participants entering the study across 5 participating sites.

It has been argued that a necessary condition for the emergence of speech in humans is the ability to vocalize irrespectively of underlying affective states, but when and how this happens during development remains unclear. To examine this, we used wearable microphones and autonomic sensors to collect multimodal naturalistic datasets from 12-month-olds and their caregivers. We observed that, across the day, clusters of vocalisations occur during elevated infant and caregiver arousal. This relationship is stronger in infants than caregivers: caregivers' vocalizations show greater decoupling with their own states of arousal, and their vocal production is more influenced by the infant’s arousal than their own. Different types of vocalisation elicit different patterns of change across the dyad. Cries occur following reduced infant arousal stability and lead to increased child-caregiver arousal coupling, and decreased infant arousal. Speech-like vocalisations also occur at elevated arousal, but lead to longer-lasting increases in arousal, and elicit more parental verbal responses. Our results suggest that: 12-month-old infants’ vocalisations are strongly contingent on their arousal state (for both cries and speech-like vocalisations), whereas adults’ vocalisations are more flexibly tied to their own arousal; that cries and speech-like vocalisations alter the intra-dyadic dynamics of arousal in different ways, which may be an important factor driving speech development; and that this selection mechanism which drives vocal development is anchored in our stress physiology. Methods Experimental participant details The project was approved by the Research Ethics Committee at the University of East London (Approval number: EXP 1617 04). Informed consent, and intent to publish, were obtained in the usual manner. Participants were recruited from the London, Essex, Hertfordshire and Cambridge regions of the UK. In total, 91 infant-caregiver dyads were recruited to participate in the study, of whom usable autonomic data were recorded from 82. Of these, usable paired autonomic data (from both caregiver and child) were obtained from 74 participants. Further details, including exclusion criteria, and detailed demographic details on the sample, are given in Appendix 1 section 1.1. The sample size was selected following power calculations presented in the original funding application ES/N017560/1. Of note, we excluded families in which the primary day-time care was performed by the male caregiver because the numbers were insufficient to provide an adequately gender-matched sample. All participating caregivers were, therefore, female. Participants received £30 in gift vouchers as a token of gratitude for participation, split over two visits. Experimental method details Participating caregivers were invited to select a day during which they would be spending the entire day with their child but which was otherwise, as far as possible, typical for them and their child. The researcher visited the participants’ homes in the morning (c. 7:30–10am) to fit the equipment, and returned later (c. 4–7pm) to pick it up. The mean (std) recording time per day was 7.3 (1.4) hours. The equipment consisted of two wearable layers, for both infant and caregiver. For the infant, a specially designed baby-grow was worn next to the skin, which contained a built-in Electrocardiogram (ECG) recording device (recording at 250Hz), accelerometer (30Hz), Global Positioning System (GPS) (1Hz), and microphone (11.6kHz). A T-shirt, worn on top of the device, contained a pocket to hold the microphone and a miniature video camera (a commercially available Narrative Clip 2 camera). For the caregiver, a specially designed chest strap was also worn next to the skin, containing the same equipment. A cardigan, worn as a top layer, contained the microphone and video camera. The clothes were comfortable when worn and, other than a request to keep the equipment dry, participants were encouraged to behave exactly as they would do on a normal day. At the start and end of each recording session, before the devices were inserted into the clothes worn by the participants, the researchers synchronised the two devices by holding them on top of one another and moving them sharply from side to side, once per second for 10 consecutive seconds. Post hoc trained coders identified the timings of these movements in the accelerometer data from each device independently. This information was used to synchronise the two recording devices. Quantification and statistical analysis Autonomic data parsing and calculation of the autonomic composite measure. Further details on the parsing of the heart rate, heart rate variability, and actigraphy are given here: https://tinyurl.com/yckzfxf8. Here we present our motivation for collapsing these three measures into a single composite measure of autonomic arousal. Home/Awake coding. Our preliminary analyses suggested that infants tended to be strapped-in to either a buggy or car seat for much of the time that they were outdoors, which strongly influenced their autonomic data. For this reason, all of the analyses presented in the paper only include data segments in which the dyad was at home and the infant was awake. A description of how these segments were identified is given in Appendix 1 (section 1.7). Following these exclusions, the mean (std) total amount of data available per dyad was 3.7 (1.7) hours, corresponding to 221.5 (102.4) 60-second epochs per dyad. Vocal coding. The microphone recorded a 5-second snapshot of the auditory environment every 60 seconds. Post hoc, trained coders identified samples in which the infant or caregiver was vocalising, and the following codings were applied. For each coding scheme, consistency of rating between coders was achieved through discussions and joint coding sessions based on an ersatz dataset, before the actual dataset was coded. All coders were blind to study design and hypothesised study outcome. Importantly, analyses conducted on a separate, continuous dataset (see Appendix 1, section S10) suggest that the temporal structure of our vocalisations was maintained despite this ‘sparse sampling’ approach. Furthermore, our analyses examine how arousal changes relative to observed vocalisations, and any arousal changes that we do observe time-locked to vocalisations would be weakened (not strengthened) by the fact that the vocalisation data were sparsely sampled (because power would have been reduced by missing vocalizations through the sparse sampling method, rather than increased). Infant data. i) vocalisation type. A morphological coding scheme was applied with the following categories: cry, laugh, squeal, growl, quasi-resonant vowel, fully-resonant vowel, marginal syllable, canonical syllable. Overall, 29% of vocalisations were cries; 1% laughs; 1% squeal; 3% growl; 18% quasi-resonant vowel; 18% fully-resonant vowel; 6% marginal syllable; 23% canonical syllable. For analyses presented in the main text these were collapsed into cries and speech-like vocalisations, which included the following non-cry categories: quasi-resonant vowel; fully-resonant vowel; marginal syllable; canonical syllable. Laughs, squeals and growls were excluded due to rarity. ii) vocal affect was coded on a three-point scale for vocal affect (negative (fussy and difficult), neutral or positive (happy and engaged). In order to assess inter-rater reliability, 11% of the sample was double coded; Cohen’s kappa was 0.70, which is considered substantial agreement. iii) vocal intensity was coded on a three-point scale from low emotional intensity, neutral, or high emotional intensity. Adult data. i) vocalisation type. A trained coder listened to vocalisations one by one and categorised them into the following categories: Imperative, Question, Praise, Singing, Imitation of Baby Vocalisation, Laughter, Reassurance, Sighing, Storytelling. These were then further collapsed into four supraordinate categories: Positive (Singing, Laughter); Stimulating (Question); Intrusive/negative affect (Imperative, Sighing); Sensitive (Praise, Imitation of Baby Vocalisation, Reassurance, Storytelling). Overall, 14% of vocalisations were Positive; 30% were Stimulating; 41% were Intrusive; 15% were Praise. In addition, ii) vocal affect and iii) vocal intensity were coded in the same way as for the infant data. In order to assess inter-rater reliability, 24% of the sample was double coded; Cohen’s kappa was 0.60, which is considered acceptable. Physical positions while vocalising. We also ascertained the physical position of our participants while vocalising (Appendix 1 section 1.8). Permutation-based temporal clustering analyses. To estimate the significance of time-series relationships, a permutation-based temporal clustering approach was used. This procedure, which is adapted from neuroimaging, allows us to estimate the probability of temporally contiguous relationships being observed in our results, a fact that standard approaches to correcting for multiple comparisons fail to account for. See further details in Appendix 1 section 1.9. ROC analyses. In order to assess the selection of visual features we employed a signal detection framework based on the Receiver Operator Characteristic (ROC) . This analyses the degree to which arousal levels predict the timings of vocalisations relative to the timings of randomly sampled comparison samples, epoch by epoch. See Results section and 67 for more details. Arousal stability. Arousal stability was measured by calculating the auto-correlation in infant and caregiver arousal, considered separately. The auto-correlation was calculated using the Matlab function nanautocorr.m, written by Fabio Oriani. Only the first lag term was reported as previous analyses have shown that autocorrelation data show a strong first order autoregressive tendency. Arousal coupling. Arousal coupling was measured by