Patient-reported outcome measures to detect intentional, mixed, or unintentional non-adherence to medication: a systematic review.

OBJECTIVE: To categorise patient-reported outcome measures (PROMs) into their propensity to detect intentional and/or unintentional non-adherence to medication, and synthesise their psychometric properties. DESIGN: Systematic review and regression analysis. ELIGIBILITY: Medication adherence levels studied at primary, secondary and tertiary care settings. Self-reported measures with scoring methods were included. Studies without proxy measures were excluded. DATA SOURCES: Using detailed searches with key concepts including questionnaires, reliability and validity, and restricted to English, MEDLINE, EMBASE, CINAHL, International Pharmaceutical Abstracts, and Cochrane Library were searched until 01 March 2022. Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA-2020) checklist was used. DATA ANALYSIS: Risk of bias was assessed via COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN-2018) guidelines. Narrative synthesis aided by graphical figures and statistical analyses. OUTCOME MEASURES: Process domains [behaviour (e.g., self-efficacy), barrier (e.g., impaired dexterity) or belief (e.g., perception)], and overall outcome domains of either intentional (I), unintentional (UI), or mixed non-adherence. RESULTS: Paper summarises evidence from 59 studies of PROMs, validated among patients aged 18-88 years in America, the United Kingdom, Europe, Middle East, and Australasia. PROMs detected outcome domains: intentional non-adherence, n=44 (I=491 criterion items), mixed intentionality, n=13 (I=79/UI=50), and unintentional, n=2 (UI=5). Process domains detected include belief (383 criterion items), barrier (192) and behaviour (165). Criterion validity assessment used proxy measures (biomarkers, e-monitors), and scoring was ordinal, dichotomised, or used Visual Analogue Scale. Heterogeneity was revealed across psychometric properties (consistency, construct, reliability, discrimination ability). Intentionality correlated positively with negative beliefs (r(57)=0.88) and barriers (r(57)=0.59). For every belief or barrier criterion-item, PROMs' aptitude to detect intentional non-adherence increased by ß=0.79 and ß=0.34 units, respectively (R2=0.94). Primary care versus specialised care predicted intentional non-adherence (OR 1.9; CI 1.01 to 2.66). CONCLUSIONS: Ten PROMs had adequate psychometric properties. Of the ten, eight PROMs were able to detect total, and two PROMs were able to detect partial intentionality to medication default. Fortification of patients' knowledge and illness perception, as opposed to daily reminders alone, is most imperative at primary care levels.

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study.

BACKGROUND: Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level. METHODS: We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk. FINDINGS: The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0·84]) or patient clinical (0·64 [0·62-0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82-0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82-0·86] to 0·88 [0·86-0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46-0·52] to 0·68 [0·64-0·70]). INTERPRETATION: Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections. FUNDING: Medical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.

Bacterial genotypic and patient risk factors for adverse outcomes in Escherichia coli bloodstream infections: a prospective molecular epidemiological study.

OBJECTIVES: Escherichia coli bloodstream infections have shown a sustained increase in England, for reasons that are unknown. Furthermore, the contribution of MDR lineages such as ST131 to overall E. coli disease burden and outcome is undetermined. METHODS: We genome-sequenced E. coli blood isolates from all patients with E. coli bacteraemia in north-west London from July 2015 to August 2016 and assigned MLST genotypes, virulence factors and AMR genes to all isolates. Isolate STs were then linked to phenotypic antimicrobial susceptibility, patient demographics and clinical outcome data to explore relationships between the E. coli STs, patient factors and outcomes. RESULTS: A total of 551 E. coli genomes were analysed. Four STs (ST131, 21.2%; ST73, 14.5%; ST69, 9.3%; and ST95, 8.2%) accounted for over half of cases. E. coli genotype ST131-C2 was associated with phenotypic non-susceptibility to quinolones, third-generation cephalosporins, amoxicillin, amoxicillin/clavulanic acid, gentamicin and trimethoprim. Among 300 patients from whom outcome was known, an association between the ST131-C2 lineage and longer length of stay was detected, although multivariable regression modelling did not demonstrate an association between E. coli ST and mortality. Several unexpected associations were identified between gentamicin non-susceptibility, ethnicity, sex and adverse outcomes, requiring further research. CONCLUSIONS: Although E. coli ST was associated with defined antimicrobial non-susceptibility patterns and prolonged length of stay, E. coli ST was not associated with increased mortality. ST131 has outcompeted other lineages in north-west London. Where ST131 is prevalent, caution is required when devising empiric regimens for suspected Gram-negative sepsis, in particular the pairing of ß-lactam agents with gentamicin.

Referral outcomes of attendances at general practitioner led urgent care centres in London, England: retrospective analysis of hospital administrative data.

OBJECTIVE: To identify patient and attendance characteristics that are associated with onwards referral to co-located emergency departments (EDs) or other hospital specialty departments from general practitioner (GP) led urgent care centres (UCCs) in northwest London, England. METHODS: We conducted a retrospective analysis of administrative data recorded in the UCCs at Charing Cross and Hammersmith Hospitals, in northwest London, from October 2009 to December 2012. Attendances made by adults resident in England were included. Logistic regression was used to model the associations between the explanatory variables-age; sex; ethnicity; socioeconomic status; area of residence; distance to UCC; GP registration; time, day, quarter, year; and UCC of attendance-and the outcome of onwards referral to the co-located EDs or other hospital specialty departments. RESULTS: Of 243 042 included attendances, 74.1% were managed solely within the UCCs without same day referral to the EDs (16.8%) or other hospital specialty departments (5.7%), or deferred referral to a fracture, hand management or soft tissue injury management clinic (3.3%). The adjusted odds of onwards referral was estimated to increase by 19% (OR 1.19, 95% CI 1.18 to 1.19) for a 10 year increase in a patient's age. Men, patients registered with a GP and residents of less socioeconomically deprived areas were also more likely to be referred onwards from the UCCs. CONCLUSIONS: The majority of patients, across each category of all explanatory variables, were managed solely within the UCCs, although a large absolute number of patients were referred onwards each year. Several characteristics of patients and their attendances were associated with the outcome variable.

Social network sites as a mode to collect health data: a systematic review.

BACKGROUND: To date, health research literature has focused on social network sites (SNS) either as tools to deliver health care, to study the effect of these networks on behavior, or to analyze Web health content. Less is known about the effectiveness of these sites as a method for collecting data for health research and the means to use such powerful tools in health research. OBJECTIVE: The objective of this study was to systematically review the available literature and explore the use of SNS as a mode of collecting data for health research. The review aims to answer four questions: Does health research employ SNS as method for collecting data? Is data quality affected by the mode of data collection? What types of participants were reached by SNS? What are the strengths and limitations of SNS? METHODS: The literature was reviewed systematically in March 2013 by searching the databases MEDLINE, Embase, and PsycINFO, using the Ovid and PubMed interface from 1996 to the third week of March 2013. The search results were examined by 2 reviewers, and exclusion, inclusion, and quality assessment were carried out based on a pre-set protocol. RESULTS: The inclusion criteria were met by 10 studies and results were analyzed descriptively to answer the review questions. There were four main results. (1) SNS have been used as a data collection tool by health researchers; all but 1 of the included studies were cross-sectional and quantitative. (2) Data quality indicators that were reported include response rate, cost, timeliness, missing data/completion rate, and validity. However, comparison was carried out only for response rate and cost as it was unclear how other reported indicators were measured. (3) The most targeted population were females and younger people. (4) All studies stated that SNS is an effective recruitment method but that it may introduce a sampling bias. CONCLUSIONS: SNS has a role in health research, but we need to ascertain how to use it effectively without affecting the quality of research. The field of SNS is growing rapidly, and it is necessary to take advantage of the strengths of this tool and to avoid its limitations by effective research design. This review provides an important insight for scholars who plan to conduct research using SNS.

Patterns of healthcare use among adolescents attending an urban general practitioner-led urgent care centre.

INTRODUCTION: Adolescence is a time of increasing health and peak fitness, as well as increasing health risks. In the UK, primary care is free at the point of access, yet, adolescents aged 10-19 years are the lowest users of primary care services, and disproportionately high users of emergency services. The effect of new general practitioner (GP)-led urgent care centres in meeting the needs of adolescents are unknown. METHODS: We used routinely collected data to describe the demographics and attendance pattern among adolescents at two new colocated GP-led urgent care centres at Hammersmith and Charing Cross Hospitals, London. We also compared attendance rates with those observed in routine general practice and emergency departments. RESULTS: Adolescents formed 6.5% (N=14 038) of total urgent care attendances. 13.2% (95% CI 12.9% to 14.1%) was recorded as not being registered with a GP. Commonest reasons for attendance were musculoskeletal conditions and injuries (30.2%), respiratory tract infections (12.5%) and limb fractures (5.1%). Adolescents aged 15-19 years were more likely to attend the centres (30.6 vs 23.4, per 100, p<0.0001) than routine general practice. The opposite was true for adolescents aged 10-14 years. CONCLUSIONS: Adolescents aged 15-19 years are more likely to attend urgent care centres than general practice. The majority attended for conditions commonly seen in primary care including musculoskeletal conditions and injuries, and respiratory tract infections. Primary care services may need to be more responsive to needs of the older adolescent age, if their use of urgent care centres is to be reduced.

Evaluation of a general practitioner-led urgent care centre in an urban setting: description of service model and plan of analysis.

Estimates of patients attending with conditions deemed non-urgent or inappropriate for accident and emergency services vary widely, from 6 to 80%. Previous research suggests that general practitioners (GPs) working in emergency departments can reduce referral rates, diagnostic testing, the proportion of patients who become emergency hospital admissions, and inappropriate attendances. However, little of this previous research is recent and new models of care for GPs working in emergency departments have now been developed, which remain to be evaluated. In this paper, we describe an integrated urgent care model, which was commissioned by NHS Hammersmith and Fulham in 2009 to manage the rising number of urgent attendances at local hospitals and its associated evaluation. The evaluation will include examining the effect of the system on outcomes such as utilization of diagnostic tests and effect on unplanned hospital admissions. If the new model of care is shown to be both clinically effective and cost-effective, the model and the proposed plan of evaluation will also be helpful to other areas that are considering the introduction of similar models of GP-led urgent care.