ObjectiveTo review the evidence on the effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality.DesignSystematic review and meta-analysis.Data sourcesMedline, Embase, CINAHL, Biosis, Joanna Briggs, Global Health, and World Health Organization COVID-19 database (preprints).Eligibility criteria for study selectionObservational and interventional studies that assessed the effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality.Main outcome measuresThe main outcome measure was incidence of covid-19. Secondary outcomes included SARS-CoV-2 transmission and covid-19 mortality.Data synthesisDerSimonian Laird random effects meta-analysis was performed to investigate the effect of mask wearing, handwashing, and physical distancing measures on incidence of covid-19. Pooled effect estimates with corresponding 95% confidence intervals were computed, and heterogeneity among studies was assessed using Cochran’s Q test and the I2 metrics, with two tailed P values.Results72 studies met the inclusion criteria, of which 35 evaluated individual public health measures and 37 assessed multiple public health measures as a “package of interventions.” Eight of 35 studies were included in the meta-analysis, which indicated a reduction in incidence of covid-19 associated with handwashing (relative risk 0.47, 95% confidence interval 0.19 to 1.12, I2=12%), mask wearing (0.47, 0.29 to 0.75, I2=84%), and physical distancing (0.75, 0.59 to 0.95, I2=87%). Owing to heterogeneity of the studies, meta-analysis was not possible for the outcomes of quarantine and isolation, universal lockdowns, and closures of borders, schools, and workplaces. The effects of these interventions were synthesised descriptively.ConclusionsThis systematic review and meta-analysis suggests that several personal protective and social measures, including handwashing, mask wearing, and physical distancing are associated with reductions in the incidence covid-19. Public health efforts to implement public health measures should consider community health and sociocultural needs, and future research is needed to better understand the effectiveness of public health measures in the context of covid-19 vaccination.Systematic review registrationPROSPERO CRD42020178692.

Background Alternative models of cancer follow-up care are needed to ameliorate pressure on services and better meet survivors’ long-term needs. This paper reports an evaluation of a service improvement initiative for the follow-up care of prostate cancer patients based on remote monitoring and supported self-management. Methods This multi-centred, historically controlled study compared patient reported outcomes of men experiencing the new Programme with men experiencing a traditional clinic appointment model of follow-up care, who were recruited in the period immediately prior to the introduction of the Programme. Data were collected by self-completed questionnaires, with follow up measurement at four and eight months post-baseline. The primary outcome was men’s unmet survivorship needs, measured by the Cancer Survivors’ Unmet Needs Survey. Secondary outcomes included cancer specific quality of life, psychological wellbeing and satisfaction with care. The analysis was intention to treat. Regression analyses were conducted for outcomes at each time point separately, controlling for pre-defined clinical and demographic variables. All outcome analyses are presented in the paper. Costs were compared between the two groups. Results Six hundred and twenty-seven men (61%) were consented to take part in the study (293 in the Programme and 334 in the comparator group.) Regarding the primary measure of unmet survivorship needs, 25 of 26 comparisons favoured the Programme, of which 4 were statistically significant. For the secondary measures of activation for self-management, quality of life, psychological well-being and lifestyle, 20 of 32 comparisons favoured the Programme and 3 were statistically significant. There were 22 items on the satisfaction with care questionnaire and 13 were statistically significant. Per participant costs (British pounds, 2015) in the 8 month follow up period were slightly lower in the programme than in the comparator group (£289 versus £327). The Programme was acceptable to patients. Conclusion The Programme is shown to be broadly comparable to traditional follow-up care in all respects, adding to evidence of the viability of such models. Electronic supplementary material The online version of this article (10.1186/s12885-019-5561-0) contains supplementary material, which is available to authorized users.

BackgroundConcerns about the degree of compassion in health care have become a focus for national and international attention. However, existing research on compassionate care interventions provides scant evidence of effectiveness or the contexts in which effectiveness is achievable.ObjectivesTo assess the feasibility of implementing the Creating Learning Environments for Compassionate Care (CLECC) programme in acute hospital settings and to evaluate its impact on patient care.DesignPilot cluster randomised trial (CRT) and associated process and economic evaluations.SettingSix inpatient ward nursing teams (clusters) in two English NHS hospitals randomised to intervention (n = 4) or control (n = 2).ParticipantsPatients (n = 639), staff (n = 211) and visitors (n = 188).InterventionCLECC is a workplace educational intervention focused on developing sustainable leadership and work team practices (dialogue, reflective learning, mutual support) theorised to support the delivery of compassionate care. The control setting involved no planned staff team-based educational activity.Main outcome measuresQuality of Interaction Schedule (QuIS) for staff–patient interactions, patient-reported evaluations of emotional care in hospital (PEECH) and nurse-reported empathy (as assessed via the Jefferson Scale of Empathy).Data sourcesStructured observations of staff–patient interactions; patient, visitor and staff questionnaires and qualitative interviews; and qualitative observations of CLECC activities.ResultsThe pilot CRT proceeded as planned and randomisation was acceptable to teams. There was evidence of potential contamination between wards in the same hospital. QuIS performed well, achieving a 93% recruitment rate, with 25% of the patient sample cognitively impaired. At follow-up there were more positive (78% vs. 74%) and fewer negative (8% vs. 11%) QuIS ratings for intervention wards than for control wards. In total, 63% of intervention ward patients achieved the lowest possible (i.e. more negative) scores on the PEECH connection subscale, compared with 79% of control group patients. These differences, although supported by the qualitative findings, are not statistically significant. No statistically significant differences in nursing empathy were observed, although response rates to staff questionnaire were low (36%). Process evaluation: the CLECC intervention is feasible to implement in practice with medical and surgical nursing teams in acute care hospitals. Strong evidence of good staff participation was found in some CLECC activities and staff reported benefits throughout its introductory period and beyond. Further impact and sustainability were limited by the focus on changing ward team behaviours rather than wider system restructuring. Economic evaluation: the costs associated with using CLECC were identified and it is recommend that an impact inventory be used in any future study.LimitationsFindings are not generalisable outside hospital nursing teams, and this feasibility work is not powered to detect differences attributable to the CLECC intervention.ConclusionsUse of the experimental methods is feasible. The use of structured observation of staff–patient interaction quality is a promising primary outcome that is inclusive of patient groups often excluded from research, but further validation is required. Further development of the CLECC intervention should focus on ensuring that it is adequately supported by resources, norms and relationships in the wider system by, for instance, improving the cognitive participation of senior nurse managers. Funding is being sought for a more definitive evaluation.Trial registrationCurrent Controlled Trials ISRCTN16789770.FundingThis project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full inHealth Services and Delivery Research; Vol. 6, No. 33. See the NIHR Journals Library website for further project information. The systematic review reported inChapter 2was funded by the NIHR Collaboration for Leadership in Applied Health Research and Care Wessex, the University of Örebro and the Karolinska Institutet.

Background: Research into relational care in hospitals will be facilitated by a focus on staff-patient interactions. The Quality of Interactions Schedule (QuIS) uses independent observers to measure the number of staff-patient interactions within a healthcare context, and to rate these interactions as 'positive social'; 'positive care'; 'neutral'; 'negative protective'; or 'negative restrictive'. QuIS was developed as a research instrument in long term care settings and has since been used for quality improvement in acute care. Prior to this study, its use had not been standardised, and reliability and validity in acute care had not been established. Methods: In 2014 and 2015 a three -phase study was undertaken to develop and test protocols for the use of QuIS across three acute wards within one NHS trust in England. The phases were: (1) A pilot of 16 h observation which developed implementation strategies for QuIS in this context; (2) training two observers and undertaking 16 h of paired observation to inform the development of training protocols; (3) training four nurses and two lay volunteers according to a finalised protocol followed by 36 h of paired observations to test inter-rater agreement. Additionally, patients were asked to rate interactions and to complete a shortened version of the Patient Evaluation of Emotional Care during Hospitalisation (PEECH) questionnaire. Results: Protocols were developed for the use of QuIS in acute care. Patients experienced an average of 6.7 interactions/patient/h (n = 447 interactions). There was close agreement between observers in relation to the number of interactions observed (Intraclass correlation coefficient (ICC) = 0.97) and moderate to substantial agreement on the quality of interactions (absolute agreement 73%, kappa 0.53 to 0.62 depending on weighting scheme). There was 79% agreement (weighted kappa 0.40: P < 0.001; indicating fair agreement) between patients and observers over whether interactions were positive, negative or neutral. Conclusions: Observers using clear QuIS protocols can achieve levels of agreement that are acceptable for the use of QuIS as a research instrument. There is fair agreement between observers and patients' rating of interactions. Further research is needed to explore the relationship between QuIS measures and reported patient experience.

BackgroundThe quality of staff-patient interactions underpins the overall quality of patient experience and can affect other important outcomes. However no studies have been identified that comprehensively explore both the quality and quantity of interactions in general hospital settings.Aims & objectivesTo quantify and characterise the quality of staff-patient interactions and to identify factors associated with negative interaction ratings.SettingData were gathered at two acute English NHS hospitals between March and April 2015. Six wards for adult patients participated including medicine for older people (n = 4), urology (n = 1) and orthopaedics (n = 1).MethodsEligible patients on participating wards were randomly selected for observation. Staff-patient interactions were observed using the Quality of Interactions Schedule. 120 h of care were observed with each 2 h observation session determined from a balanced random schedule (Monday-Friday, 08:00-22:00 h). Multilevel logistic regression models were used to determine factors associated with negative interactions.Results1554 interactions involving 133 patients were observed. The median length of interaction was 36 s with a mean of 6 interactions per patient per hour. Seventy three percent of interactions were categorized as positive, 17% neutral and 10% negative. Forty percent of patients had at least one negative interaction (95% confidence interval 32% to 49%). Interactions initiated by the patient (adjusted Odds Ratio [OR] 5.30), one way communication (adjusted OR 10.70), involving two or more staff (adjusted OR 5.86 for 2 staff, 6.46 for 3+ staff), having a higher total number of interactions (adjusted OR 1.09 per unit increase), and specific types of interaction content were associated with increased odds of negative interaction (p < 0.05). In the full multivariable model there was no significant association with staff characteristics, skill mix or staffing levels. Patient agitation at the outset of interaction was associated with increased odds of negative interaction in a reduced model. There was no significant association with gender, age or cognitive impairment. There was substantially more variation at ward level (variance component 1.76) and observation session level (3.49) than at patient level (0.09).ConclusionThese findings present a unique insight into the quality and quantity of staff-patient interactions in acute care. While a high proportion of interactions were positive, findings indicate that there is scope for improvement. Future research should focus on further exploring factors associated with negative interactions, such as workload and ward culture.