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Retention in Care

Retention in Care refers to the consistent engagement and active participation of individuals in their healthcare management over time.
This involves patients regularly attending scheduled appointments, adhering to treatment plans, and maintaining open communication with their healthcare providers.
Effective retention strategies are crucial for managing chronic conditions, improving health outcomes, and reducing the risk of disease progression or complications.
By optimizing retention in care, healthcare providers can enhance patients' quality of life, foster better treatment adherence, and ultimately, contribute to more successful long-term disease managment.

Most cited protocols related to «Retention in Care»

1
Retention in Care (RIC) Intervention Study
Cited 26 times
Clinic-wide, patient-level data were used from six academically-affiliated HIV medical clinics participating in a CDC and HRSA sponsored Retention in Care (RIC) intervention study, which has previously been described in detail.21 ,22 The six study sites include HIV clinics affiliated with Baylor College of Medicine, Houston, TX; Boston University Medical Center, Boston, MA; Johns Hopkins University, Baltimore, MD, State University of New York, Downstate Medical Center, Brooklyn, NY; University of Alabama at Birmingham, Birmingham, AL; and University of Miami, Miami, FL. The current study used systematically captured, de-identified, socio-demographic, clinical and medical visit patient-level data from a 2-year period preceding implementation of the intervention. The analysis included patients at the six study sites who had: (1) at least one scheduled primary HIV care appointment during the first 6 months (189 days) of a 12-month observation period (1 May 2008 – 30 April 2009) and (2) attended at least 1 primary HIV medical provider visit in the year preceding the observation period (1 May 2007 – 30 April 2008). These criteria were employed to identify established clinic patients in whom retention in care could be measured. The RIC research protocol received Institutional Review Board approval at all study sites.
Mugavero M.J., Westfall A.O., Zinski A., Davila J., Drainoni M.L., Gardner L.I., Keruly J.C., Malitz F., Marks G., Metsch L., Wilson T.E, & Giordano T.P. (2012). Measuring Retention in HIV Care: The Elusive Gold Standard. Journal of acquired immune deficiency syndromes (1999), 61(5), 574-580.
Publication 2012
Ethics Committees, Research HIV-1 Patients Pharmaceutical Preparations Primary Health Care Retention (Psychology) Retention in Care
2
Factors Influencing HIV Care Retention
Cited 15 times
We provide an overview of data collection for the original study as context for our analyses on saturation of these data. The research question of the original study was: what influences patient retention in HIV care? With the advent of antiretroviral therapy (ART), HIV infection has transitioned from a fatal disease to a chronic condition. ART is important for slowing progression of the disease and reducing HIV transmission to others (Attia, Egger, Müller, Zwahlen, & Low, 2009 (link); Cohen et al., 2011 (link); “Vital Signs,” 2011 (link)). Becoming linked to care soon after diagnosis with HIV is critical for early initiation of ART and regular monitoring of the viral load and other comorbidities. However, only 77% of those known to be HIV positive in the United States are linked to care, and only 51% are retained in regular care thereafter (Hall et al., 2012 (link); “Vital Signs,” 2011 (link)). Therefore, the aim of the original study was to understand what influences retention in HIV care at the Infectious Disease Clinic (IDC) of the Atlanta VA Medical Center (AVAMC), the largest VA clinic caring for HIV-positive patients in the United States.
Hennink M.M., Kaiser B.N, & Marconi V.C. (2016). Code Saturation Versus Meaning Saturation: How Many Interviews Are Enough?. Qualitative health research, 27(4), 591-608.
Publication 2016
Communicable Diseases Diagnosis Disease, Chronic Disease Progression Early Therapy HIV Infections HIV Seropositivity Patients Retention in Care Signs, Vital Therapeutics Transmission, Communicable Disease
3
HIV Progression and Treatment Modeling in Resource-Limited Settings
Cited 11 times
CEPAC-I is a microsimulation of HIV disease progression and
treatment in resource-limited settings (5 (link)). CEPAC-I aggregates the experience of large numbers of
individual patients to project the population-level impact of alternative
treatment scale-up policies (6 (link)).
Individual patient characteristics (e.g., age, gender, CD4 count, HIV RNA)
are generated by random draws from distributions, some of which (e.g., CD4)
are dependent on the simulated patient’s stage in the HIV continuum
of care at model outset (i.e. not in care, on ART suppressed, on ART not
suppressed, Table 1, top). CEPAC-I
simulates the success and failure of disease detection, linkage to care, ART
initiation, virologic suppression, and retention in care. It also simulates
risks of opportunistic infections (OI), which increase with falling CD4
counts. Undetected or otherwise unlinked patients experience immunologic/CD4
decline. HIV is detected through testing or the development of an OI. Only
patients achieving virologic suppression on ART experience CD4 increases;
unsuppressed ART patients achieve no CD4 recovery and continue to face
reduced CD4-specific OI risk while accruing ART costs (5 (link)).
For those initiating ART, the 48-week virologic suppression rate is
72% (2 (link), 10 (link)). HIV RNA is monitored according to South
African guidelines (11 ). Patients in
whom ART failure is detected may be switched to a second-line regimen, if
one is available. Patients in care are at an adherence-dependent risk of
loss-to-follow-up at monthly rates ranging from 0.2–1.1%,
resulting in an 80% probability of remaining in care at 5 years
(12 (link)–14 (link)). Additional model detail – including
model figures, flowcharts, state space definitions, user guides, data
fields, protocols for data assembly, and stopping rules – can be
found at: http://www.massgeneral.org/mpec/cepac/.
Walensky R.P., Borre E.D., Bekker L.G., Resch S.C., Hyle E.P., Wood R., Weinstein M.C., Ciaranello A.L., Freedberg K.A, & Paltiel A.D. (2016). The Anticipated Clinical and Economic Impact of 90-90-90 in South Africa. Annals of internal medicine, 165(5), 325-333.
Publication 2016
CD4+ Cell Counts Disease Progression Gender Opportunistic Infections Patients Retention in Care Treatment Protocols
4
Evaluating Early Infant HIV Diagnosis Algorithms in South Africa
Cited 10 times
We used the Cost-Effectiveness of Preventing AIDS Complications (CEPAC)–Pediatric model to simulate a cohort of HIV-exposed infants in South Africa undergoing 2 EID algorithms: 6-week EID testing without confirmatory testing and 6-week EID testing with confirmatory testing. The CEPAC–Pediatric model is a first-order Monte Carlo simulation model of paediatric HIV infection, disease progression, diagnosis, and treatment [19 (link)–21 (link)]. For this analysis, we simulated HIV-exposed infants (born to women living with HIV) from birth through death. Risk of intrauterine or intrapartum HIV infection was modelled as a 1-time risk, based on 3 key maternal characteristics: the probability a mother was aware of her HIV diagnosis during pregnancy; the probability that she received ART during pregnancy, reflecting prevention of MTCT (PMTCT) coverage; and maternal CD4 count for women not receiving ART, reflecting disease stage. Uninfected infants faced a monthly risk of postpartum transmission based on these same characteristics until complete cessation of breastfeeding. All simulated patients faced monthly risks of non-HIV-related mortality. After HIV infection occurred, patients faced additional risks of opportunistic infections (OIs) and HIV-related mortality based on their age, CD4 percent (age < 5 years) or CD4 count (age ≥ 5 years), retention in care, and ART use. Full details of the CEPAC–Pediatric model structure are available in S1 Text and S2 Table and at http://web2.research.partners.org/cepac/model.html.
This work was approved by the Partners Human Research Committee, Boston, MA, US.
Dunning L., Francke J.A., Mallampati D., MacLean R.L., Penazzato M., Hou T., Myer L., Abrams E.J., Walensky R.P., Leroy V., Freedberg K.A, & Ciaranello A. (2017). The value of confirmatory testing in early infant HIV diagnosis programmes in South Africa: A cost-effectiveness analysis. PLoS Medicine, 14(11), e1002446.
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Publication 2017
Acquired Immunodeficiency Syndrome CD4+ Cell Counts Childbirth Diagnosis Disease Progression HIV Infections Homo sapiens Infant Mothers Patients Pregnancy Retention in Care Transmission, Communicable Disease Woman
5
Comprehensive HIV Care for Pregnant Women
Cited 10 times
The design of MCH-ART is composed of 3 interrelated phases with observational and experimental elements, in which HIV-infected pregnant women are followed up during the antenatal and postnatal periods (Fig. 1). Throughout, participants attend study measurement visits conducted separately from routine ART service appointments.
Phase 1 is a cross-sectional evaluation of consecutively enrolled HIV-infected pregnant women seeking antenatal care. This phase of the study allows characterization of the health status of the population of HIV-infected pregnant women seeking care at the Gugulethu MOU and the services they receive. Phase 2 of the study is an observational cohort of all women in phase 1 who are eligible for initiation of ART (following local public sector guidelines), from their second antenatal clinic visit until their first postpartum clinic visit (conducted within 7 days postpartum). This phase of the study provides detailed description of ART initiation and antenatal follow-up in the population of women who will be involved in the postnatal component of the study.
Phase 3 of the study is a randomized trial of strategies for delivering ART to women during the postpartum period. Women enrolled in phase 2 who are breastfeeding their infants (regardless of infant HIV status) are approached to participate in the trial at the first routine postpartum clinic visit. Consenting eligible women are randomized to 1 of 2 approaches to providing ART:

Referral to general adult ART services from approximately 4–8 weeks postpartum (the local standard of care), or,

Continued receipt of ART in the antenatal clinic, as part of an MCH-focused ART service that only refers women to general adult ART services after the end of breastfeeding.

Women participating in phase 3 are asked to return for 5 additional study visits at approximately 6 weeks, 3, 6, 9, and 12 months postpartum. The primary outcomes are assessed through 12 months postpartum, and further follow-up of the cohort through 18 months postpartum is planned to assess long-term maternal retention in care after transfer to general adult ART services.
The sample sizes for each phase are shown in Figure 1. An estimated 480 women are required in phase 3 to detect a 15% absolute difference in the combined endpoint of maternal viral suppression and retention in care through 12 months postpartum. To achieve this sample size, we anticipated enrolling up to 600 women in phase 2, and for this, a maximum of 1600 women were enrolled into phase 1. Ethical approval for the study, including the informed consent process, was provided by the Human Research Ethics Committee of the University of Cape Town, Faculty of Health Sciences and the Columbia University Medical Centre Institutional Review Board.
Myer L., Phillips T.K., Zerbe A., Ronan A., Hsiao N.Y., Mellins C.A., Remien R.H., Le Roux S.M., Brittain K., Ciaranello A., Petro G., McIntyre J.A, & Abrams E.J. (2016). Optimizing Antiretroviral Therapy (ART) for Maternal and Child Health (MCH): Rationale and Design of the MCH-ART Study. Journal of Acquired Immune Deficiency Syndromes (1999), 72(Suppl 2), S189-S196.
Publication 2016
Adult Care, Prenatal Clinic Visits Ethics Committees, Research Faculty Homo sapiens Infant Population Health Pregnant Women Public Sector Retention in Care Woman

Most recents protocols related to «Retention in Care»

1
Adolescent HIV Retention in Care: Predictors Analysis
The extracted data from the ePMS was matched and merged with the patient data obtained from the PCBs using the unique ART numbers allocated (unique patient identifiers) at each ART clinic. A Microsoft Excel (Microsoft Corporation, Washington, DC, USA) spreadsheet was created from the merged data. The extracted anonymized data was saved into a password-protected excel file to prevent any unauthorized access or alterations of the data. The merged complete excel spreadsheet was imported into the SPSS statistical software (IBM SPSS version 28, IBM Corp. USA) for analysis. Descriptive statistics were carried out to describe the demographic and clinical characteristics of the adolescent participants included in the study, at baseline and during the following 36-month period. Bivariate analysis was executed utilizing the Chi-square test to determine the association, and the significance thereof, between retention in care and the demographic and clinical variables (age, sex, duration on ART, age at ART initiation, HIV disclosure status, WHO stage at ART initiation, current ART regimen, ART regimen at initiation, TB screening results, viral suppression status and healthcare facility level). Comparisons were performed between retention in care and demographic and clinical parameters at 6, 12, 18, 24 and 36 months post initiation on ART. Fisher’s exact test was used as an alternative to the Chi-square test in instances of sparse data (< 5 in any cell).
The Cox regression (Cox Proportional Hazards model) analysis was performed to adjust for potential confounders and interactions, to determine predictors for retention in care. The Cox proportional hazard model utilized the backward stepwise analysis, with the initial model inclusive of all candidate variables. The least significant variable was subsequently removed at each iteration until none of the nonsignificant variables remained. Variables were removed from the model at a set significance level of p < 0.05. The Complete Case Analysis (CCA) was used as less than 5% of the cases had missing data on all variables in the main analysis. Survival analysis was assessed with “Patient retention status at the end of the period” as the outcome of interest. A comparative survival analysis for the age and sex of the study participants using Kaplan-Meier survival curves was conducted [24 ]. Using Cox regression, factors influencing retention in care at months 6, 12, 18, 24 and 36 were established. Both the unadjusted and adjusted hazard ratios and their p-values were computed.
Munyayi F.K, & van Wyk B.E. (2023). Determinants and rates of retention in HIV care among adolescents receiving antiretroviral therapy in Windhoek, Namibia: a baseline cohort analysis. BMC Public Health, 23, 458.
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Publication 2023
Adolescent Cells Inclusion Bodies Patients Procarbazine Retention (Psychology) Retention in Care Treatment Protocols
2
Retention in HIV Care Among Adolescents
The primary outcome of this study was retention in HIV care. Longitudinal data were collected from routine clinic visits of all adolescents enrolled for ART in the Windhoek district. We considered ‘point’ retention at 6 months post-ART initiation, which can be defined as alive or the presence of individuals in ART services six months after initiating ART or any time thereafter [22 ]. We considered 36-month retention in care from January 2019 to December 2021, using six monthly intervals. The programmatic definition using the six months ‘point’ retention was preferred to align with recent guidance on multi-month prescription and multi-month dispensing of ART in Namibia [23 ]. The predictor variables extracted from the electronic database include age, sex, WHO stage at ART initiation, age at ART initiation, ART regimen at initiation, current ART regimen, duration on ART, TB screening results, HIV disclosure status, HIV viral load results and healthcare facility level.
Munyayi F.K, & van Wyk B.E. (2023). Determinants and rates of retention in HIV care among adolescents receiving antiretroviral therapy in Windhoek, Namibia: a baseline cohort analysis. BMC Public Health, 23, 458.
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Publication 2023
Adolescent Retention (Psychology) Retention in Care Treatment Protocols
3
Retention in Antiretroviral Therapy Care
In this study, we defined retention in ART care as the proportion of the infected patients who are remaining alive and are still receiving health care at study facilities after they were initiated on therapy.2 ,9 (link) Patients were classified as not being on ART at the time they stopped treatment, as being lost to follow-up (LTFU), or as having died at the study facility
Nimwesiga C., Taremwa I.M., Nakanjako D, & Nasuuna E. (2023). Factors Associated with Retention in HIV Care Among HIV-Positive Adolescents in Public Antiretroviral Therapy Clinics in Ibanda District, Rural South Western Uganda. HIV/AIDS (Auckland, N.Z.), 15, 71-81.
Publication 2023
Patients Retention in Care
4
Feasibility Trial of Cognitive Stimulation for Dementia
Recruitment and retention of care homes, residents, and care-staff in both intervention and control conditions will be calculated to inform the number of care homes needed to achieve the target sample size for a future definitive trial. Acceptability, fidelity, and adherence to COG-D intervention protocol will be evaluated with completion rates for staff training and COG-D assessments, use and display of Daisies and revisions to care-plan based on COG-D assessments.
Feasibility of a Health Economic assessment will be explored in the ability to collect the data required (COG-D use, resource use, health and quality of life of residents) to undertake a cost-effectiveness analysis in the definitive trial and estimate potential costs and health benefits of the intervention.
Demographic data for residents (age, ethnicity, length of time in care home, gender, dementia diagnosis) will be collected from care plans. For staff, data will be obtained as part of the anonymous staff questionnaire (further training completed, duration of employment at care home) or in a short questionnaire preceding the staff training (role in care home, ethnicity (to explore diversity), age category, gender).
Candidate outcome measures for residents (by proxy) will be collected in interviews with researchers and staff outcomes are collected in the anonymous staff questionnaire (self-completed). Data completion rates of the candidate outcome measures (at each time point) will be calculated to determine which is the most appropriate primary outcome, agree other study measures and inform sample size calculation for the definitive trial.
Pollux P.M., Surr C., Cohen J., Huang C., Wolverson E., Mountain P., Turner R., Hawkesford-Webb E., Winter B, & Hudson J.M. (2023). The Cognitive Daisy (COG-D) for improving care for residents with dementia in care homes: protocol of a feasibility RCT. Pilot and Feasibility Studies, 9, 34.
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Publication 2023
Dementia Diagnosis Ethnicity Gender Infantile Neuroaxonal Dystrophy Retention (Psychology) Retention in Care
5
Exploring Perspectives on HIV Care
In-depth interviews will be conducted with 3 key stakeholder groups: patient supporters, study participants, and clinic leadership and public health officials. Interviews with patient supporters will occur before and 12 months after the implementation of mLAR to assess the nature of service delivery, client interactions and how this differs from standard linkage activities, barriers and facilitators of engaging out-of-care patients, challenges and solutions to the service delivery, and recommendations for service delivery improvements and expansion of reach. We will also conduct interviews with study participants randomized to the control or intervention arms. All participants will be asked about their experiences living with and engaging in HIV care, including facilitators and barriers to appointments and medication adherence, past experiences dropping out of care, impressions of HIV-related services they received, as well as recommendations for additional services. Participants randomized to the intervention arm will also be asked about their experience using the app, their satisfaction with features of the app such as the automated messaging they received, their motivation for engaging the patient supporter–delivered SMS text messages, their perception of the impact of app usage on their care, and their suggestions for other ways the mobile phone may facilitate HIV retention in care and adherence to medications. We will also assess facilitators and barriers to app use. Interviews with clinic leaders and public health officials will explore perceptions of the intervention’s integration into linkage and engagement of care activities at clinics or the health department, as well as recommendations for service delivery improvements. These interviews will provide insights about policy, acceptability, political buy-in for the intervention, and the potential for sustainability and adoption in other settings going forward.
Jones J., McKenzie-White J., Saxton R., Grieb S.M., Nonyane B., Graham C., Cano A., Johnson S., Childs L., Greenbaum A., Flynn C., Pearlowitz M., Celano S., Chang L.W, & Page K.R. (2023). Leveraging mHealth and Patient Supporters for African Americans' and Latinxs' Engagement in HIV Care (LEAN): Protocol for a Randomized, Controlled, Effectiveness-Implementation Trial. JMIR Research Protocols, 12, e42691.
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Publication 2023
Motivation Natural Childbirth Obstetric Delivery Patients Retention in Care Satisfaction

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More about "Retention in Care"

Retention in care is a critical aspect of healthcare management, ensuring consistent patient engagement and active participation in their treatment over time.
This involves regularly attending scheduled appointments, adhering to prescribed treatment plans, and maintaining open communication with healthcare providers.
Effective retention strategies are crucial for managing chronic conditions, improving health outcomes, and reducing the risk of disease progression or complications.
By optimizing retention in care, healthcare providers can enhance patients' quality of life, foster better treatment adherence, and contribute to more successful long-term disease management.
This is particularly important for conditions that require ongoing monitoring and management, such as diabetes, hypertension, and HIV/AIDS.
Retention in care is often measured using various statistical software, such as SAS 9.4, Stata version 13, Stata 12.0, STATA version 12, Stata 16, Stata, Stata/SE software version 13, and SPSS Statistics for Windows, Version 24.0.
These tools can help healthcare providers analyze patient data, identify patterns, and develop targeted interventions to improve retention rates.
Factors that can influence retention in care include patient demographics, socioeconomic status, comorbidities, transportation challenges, and the overall quality of the healthcare system.
Addressing these barriers through patient-centered approaches, such as providing transportation assistance, offering flexible appointment scheduling, and improving communication between patients and providers, can significantly enhance retention in care.
Additionally, the use of reduced glutathione, a naturally occurring antioxidant, has been studied for its potential to support overall health and well-being, which may positively impact patient engagement and retention in care.
Stata 11 is another statistical software that can be used to analyze the relationship between reduced glutathione and various health outcomes.
By understanding the importance of retention in care and utilizing the insights gained from statistical analysis, healthcare providers can develop and implement effective strategies to improve patient engagement, enhance treatment adherence, and ultimately, contribute to better long-term health outcomes.