Hospital-based preventative interventions for people experiencing homelessness in high-income countries: A systematic review

BACKGROUND: People experiencing homelessness have significant unmet needs and high rates of unplanned care. We aimed to describe preventative interventions, defined in their broadest sense, for people experiencing homelessness in a hospital context. Secondary aims included mapping outcomes and assessing intervention effectiveness. METHODS: We searched online databases (MEDLINE, Embase, PsycINFO, HMIC, CINAHL, Web of Science, Cochrane Library) from 1999–2019 and conducted backward and forward citation searches to 31 December 2020 (PROSPERO CRD42019154036). We included quantitative studies in emergency and inpatient settings measuring health or social outcomes for adults experiencing homelessness in high income countries. We assessed rigour using the “Quality Assessment Tool for Quantitative Studies” and summarised findings using descriptive quantitative methods, a binomial test, a Harvest Plot, and narrative synthesis. We used PRISMA and SWiM reporting guidelines. FINDINGS: Twenty-eight studies identified eight intervention types: care coordination (n=18); advocacy, support, and outreach (n=13); social welfare assistance (n=13); discharge planning (n=12); homelessness identification (n=6); psychological therapy and treatment (n=6); infectious disease prevention (n=5); and screening, treatment, and referrals (n=5). The evidence strength was weak (n=16) to moderate (n=10), with two high quality randomised controlled trials. We identified six outcome categories with potential benefits observed for psychosocial outcomes, including housing (11/13 studies, 95%CI=54.6–98.1%, p=0.023), healthcare use (14/17, 56.6–96.2%, p=0.013), and healthcare costs (8/8, 63.1–100%, p=0.008). Benefits were less likely for health outcomes (4/5, 28.3–99.5%, p=0.375), integration with onward care (2/4, 6.8–93.2%, p=1.000), and feasibility/acceptability (5/6, 35.9–99.6%, p=0.219), but confidence intervals were very wide. We observed no harms. Most studies showing potential benefits were multi-component interventions. INTERPRETATION: Hospital-based preventative interventions for people experiencing homelessness are potentially beneficial, but more rigorous research is needed. In the context of high needs and extreme inequities, policymakers and healthcare providers may consider implementing multi-component preventative interventions. FUNDING: SL is supported by an NIHR Clinical Doctoral Research Fellowship (ICA-CDRF-2016-02-042). JD is supported by an NIHR School of Public Health Research Pre-doctoral Fellowship (NU-004252). RWA is supported by a Wellcome Clinical Research Career Development Fellowship (206602)

Patient/family views on data sharing in rare diseases: study in the European LeukoTreat project.: Survey assessing data sharing in leukodystrophies

International audienceThe purpose of this study was to explore patient and family views on the sharing of their medical data in the context of compiling a European leukodystrophies database. A survey questionnaire was delivered with help from referral centers and the European Leukodystrophies Association, and the questionnaires returned were both quantitatively and qualitatively analyzed. This study found that patients/families were strongly in favor of participating. Patients/families hold great hope and trust in the development of this type of research. They have a strong need for information and transparency on database governance, the conditions framing access to data, all research conducted, partnerships with the pharmaceutical industry, and they also need access to results. Our findings bring ethics-driven arguments for a process combining initial broad consent with ongoing information. On both, we propose key item-deliverables to database participants

Hospital readmission among people experiencing homelessness in England: a cohort study of 2772 matched homeless and housed inpatients

BackgroundInpatients experiencing homelessness are often discharged to unstable accommodation or the street, which may increase the risk of readmission.MethodsWe conducted a cohort study of 2772 homeless patients discharged after an emergency admission at 78 hospitals across England between November 2013 and November 2016. For each individual, we selected a housed patient who lived in a socioeconomically deprived area, matched on age, sex, hospital, and year of discharge. Counts of emergency readmissions, planned readmissions, and Accident and Emergency (A&amp;E) visits post-discharge were derived from national hospital databases, with a median of 2.8 years of follow-up. We estimated the cumulative incidence of readmission over 12 months, and used negative binomial regression to estimate rate ratios.ResultsAfter adjusting for health measured at the index admission, homeless patients had 2.49 (95% CI 2.29 to 2.70) times the rate of emergency readmission, 0.60 (95% CI 0.53 to 0.68) times the rate of planned readmission and 2.57 (95% CI 2.41 to 2.73) times the rate of A&amp;E visits compared with housed patients. The 12-month risk of emergency readmission was higher for homeless patients (61%, 95% CI 59% to 64%) than housed patients (33%, 95% CI 30% to 36%); and the risk of planned readmission was lower for homeless patients (17%, 95% CI 14% to 19%) than for housed patients (30%, 95% CI 28% to 32%). While the risk of emergency readmission varied with the reason for admission for housed patients, for example being higher for admissions due to cancers than for those due to accidents, the risk was high across all causes for homeless patients.ConclusionsHospital patients experiencing homelessness have high rates of emergency readmission that are not explained by health. This highlights the need for discharge arrangements that address their health, housing and social care needs.</jats:sec

Cardiovascular disease in homeless versus housed individuals: a systematic review of observational and interventional studies

Objectives: To identify: (i) risk of cardiovascular disease (CVD) in homeless versus housed individuals and (ii) interventions for CVD in homeless populations. Methods: We conducted a systematic literature review in EMBASE until December 2018 using a search strategy for observational and interventional studies without restriction regarding languages or countries. Meta-analyses were conducted, where appropriate and possible. Outcome measures were all-cause and CVD mortality, and morbidity. Results: Our search identified 17 articles (6 case-control, 11 cohort) concerning risk of CVD and none regarding specific interventions. Nine were included to perform a meta-analysis. The majority (13/17, 76.4%) were high quality and all were based in Europe or North America, including 765 459 individuals, of whom 32 721 were homeless. 12/17 studies were pre-2011. Homeless individuals were more likely to have CVD than nonhomeless individuals (pooled OR 2.96; 95% CI 2.80 to 3.13; p<0.0001; heterogeneity p<0.0001; I 2 =99.1%) and had increased CVD mortality (age-standardised mortality ratio range: 2.6–6.4). Compared with nonhomeless individuals, hypertension was more likely in homeless people (pooled OR 1.38–1.75, p=0.0070; heterogeneity p=0.935; I 2 =0.0%). Conclusions: Homeless people have an approximately three times greater risk of CVD and an increased CVD mortality. However, there are no studies of specific pathways/interventions for CVD in this population. Future research should consider design and evaluation of tailored interventions or integrating CVD into existing intervention

Health care in a technological world

Ongoing innovation in the field of mobile health (mHealth) has the potential to change the landscape of healthcare practice both inside and outside formal clinical settings. mHealth could enhance patient education, prevent disease, improve diagnosis and research-based treatment, reduce healthcare costs and enable patients to manage long-term conditions. In addition, unimpeded by geographical distance, smartphone-linked wearable sensors, built around real-time connectivity, will improve communication between health professionals and improve the understanding of physiological variability, thereby improving clinical decisions and quality of care. However, despite the potential of mHealth, there are still significant information gaps regarding its long-term effects, acceptability, costs and risks. There is therefore a significant need for continued research to evaluate the functions and appropriateness of these medical devices that are slowly entering the healthcare arena, while not neglecting the rights of the individual

A review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures

Background: Retaining participants in cohort studies with multiple follow-up waves is difficult. Commonly, researchers are faced with the problem of missing data, which may introduce biased results as well as a loss of statistical power and precision. The STROBE guidelines von Elm et al. (Lancet, 370:1453-1457, 2007); Vandenbroucke et al. (PLoS Med, 4:e297, 2007) and the guidelines proposed by Sterne et al. (BMJ, 338:b2393, 2009) recommend that cohort studies report on the amount of missing data, the reasons for non-participation and non-response, and the method used to handle missing data in the analyses. We have conducted a review of publications from cohort studies in order to document the reporting of missing data for exposure measures and to describe the statistical methods used to account for the missing data. Methods: A systematic search of English language papers published from January 2000 to December 2009 was carried out in PubMed. Prospective cohort studies with a sample size greater than 1,000 that analysed data using repeated measures of exposure were included. Results: Among the 82 papers meeting the inclusion criteria, only 35 (43%) reported the amount of missing data according to the suggested guidelines. Sixty-eight papers (83%) described how they dealt with missing data in the analysis. Most of the papers excluded participants with missing data and performed a complete-case analysis (n = 54, 66%). Other papers used more sophisticated methods including multiple imputation (n = 5) or fully Bayesian modeling (n = 1). Methods known to produce biased results were also used, for example, Last Observation Carried Forward (n = 7), the missing indicator method (n = 1), and mean value substitution (n = 3). For the remaining 14 papers, the method used to handle missing data in the analysis was not stated. Conclusions: This review highlights the inconsistent reporting of missing data in cohort studies and the continuing use of inappropriate methods to handle missing data in the analysis. Epidemiological journals should invoke the STROBE guidelines as a framework for authors so that the amount of missing data and how this was accounted for in the analysis is transparent in the reporting of cohort studies. © 2012 Karahalios et al.; licensee BioMed Central Ltd