A Cross-Sectional Study of Factors Influencing Pre-Operative Anxiety in Orthognathic Patients.

Orthognathic treatment is an important treatment modality to manage severe dentofacial discrepancies. Patients awaiting orthognathic surgery often experience increased anxiety, which may adversely affect post-operative recovery and treatment satisfaction. This study investigated the effects of a number of factors on pre-operative anxiety in orthognathic patients. Seventy patients prospectively recruited from three orthognathic centres in the UK completed a pre-operative questionnaire that included validated scales for measuring anxiety, social support, resilience, and coping styles. Sociodemographic data and satisfaction with the information provided by the clinical team were also elicited from the questionnaire. Univariable analysis showed that social support from a significant other (p = 0.026), resilience (p < 0.001), and satisfaction with the information provided by the clinical team (p = 0.002) were significantly associated with reduced anxiety, whilst avoidance coping (p < 0.001) and coping through seeking social support (p = 0.006) were significantly related to increased anxiety. With the exception of coping by seeking social support, these relationships retained significance in a multivariable regression analysis. Neither gender nor ethnicity moderated the effects of social support on pre-operative anxiety. These findings suggest potential avenues for clinicians to address with future interventions to reduce pre-operative anxiety. Further qualitative research may provide greater clarity on the relationship between these variables and anxiety.

Social support and pre-operative anxiety in patients undergoing elective surgical procedures: A systematic review and meta-analysis.

Pre-operative anxiety may adversely affect post-operative recovery and treatment satisfaction. This systematic review assessed the impact of social support on pre-operative anxiety in elective surgery patients. MEDLINE via Ovid, Embase, PsycINFO, Web of Science, CINAHL Plus, Emcare and LILACS were searched for publications (1950-2021). Fourteen studies were included for descriptive analysis and five for meta-analysis. The pooled estimate in the meta-analysis was r = -0.372 (95% CI: -0.578 to -0.122). Stronger social support was weakly associated with reduced pre-operative anxiety, but the quality of available evidence was low. The findings suggest potential benefit in enhancing utilisation of support networks before elective surgery.

A deterministic approach for protecting privacy in sensitive personal data.

BACKGROUND: Data privacy is one of the biggest challenges for any organisation which processes personal data, especially in the area of medical research where data include sensitive information about patients and study participants. Sharing of data is therefore problematic, which is at odds with the principle of open data that is so important to the advancement of society and science. Several statistical methods and computational tools have been developed to help data custodians and analysts overcome this challenge. METHODS: In this paper, we propose a new deterministic approach for anonymising personal data. The method stratifies the underlying data by the categorical variables and re-distributes the continuous variables through a k nearest neighbours based algorithm. RESULTS: We demonstrate the use of the deterministic anonymisation on real data, including data from a sample of Titanic passengers, and data from participants in the 1958 Birth Cohort. CONCLUSIONS: The proposed procedure makes data re-identification difficult while minimising the loss of utility (by preserving the spatial properties of the underlying data); the latter means that informative statistical analysis can still be conducted.

Patient, clinician and independent observer perspectives of shared decision making in adult orthodontics.

OBJECTIVES: To investigate and compare the extent of shared decision making (SDM) in orthodontics from the perspective of patients, clinicians and independent observers. DESIGN: A cross-sectional, observational study. SETTING: NHS teaching hospital. PARTICIPANTS: A total of 31 adult patients and their treating clinicians were included in the study. METHODS: The extent of SDM in new patient orthodontic consultations was measured using three versions of a validated instrument: the self-administered patient dyadic-OPTION scale; the self-administered clinician dyadic-OPTION scale; and an independent observer-rated OPTION12 scale. Patients and clinicians completed the 12-item dyadic-OPTION questionnaire independently at the end of the consultation to rate their perceived levels of SDM. The consultations were also audio-recorded and two calibrated raters independently rated the extent of SDM in these consultations using the OPTION12 scale. RESULTS: There was excellent inter-rater reliability between the two independent raters using the OPTION12 scale (intraclass correlation coefficient (ICC) = 0.909). The mean patient, clinician and independent observer OPTION scores for SDM were 90.4% (SD 9.1%, range 70.8% to 100%), 76.2% (SD 8.95%, range 62.5% to 95.8%) and 42.6% (SD 17.4%, range 13.5% to 68.8%), respectively. There was no significant correlation between the OPTION scores for the three groups (ICC = -0.323). CONCLUSIONS: The results showed that generally high levels of SDM were perceived by patients and clinicians but lower levels of SDM were scored by the independent observers. However, it could be argued that the patient's perception of SDM is the most important measure as it is their care that is affected by their involvement.

Privacy preserving data visualizations.

Data visualizations are a valuable tool used during both statistical analysis and the interpretation of results as they graphically reveal useful information about the structure, properties and relationships between variables, which may otherwise be concealed in tabulated data. In disciplines like medicine and the social sciences, where collected data include sensitive information about study participants, the sharing and publication of individual-level records is controlled by data protection laws and ethico-legal norms. Thus, as data visualizations - such as graphs and plots - may be linked to other released information and used to identify study participants and their personal attributes, their creation is often prohibited by the terms of data use. These restrictions are enforced to reduce the risk of breaching data subject confidentiality, however they limit analysts from displaying useful descriptive plots for their research features and findings. Here we propose the use of anonymization techniques to generate privacy-preserving visualizations that retain the statistical properties of the underlying data while still adhering to strict data disclosure rules. We demonstrate the use of (i) the well-known k-anonymization process which preserves privacy by reducing the granularity of the data using suppression and generalization, (ii) a novel deterministic approach that replaces individual-level observations with the centroids of each k nearest neighbours, and (iii) a probabilistic procedure that perturbs individual attributes with the addition of random stochastic noise. We apply the proposed methods to generate privacy-preserving data visualizations for exploratory data analysis and inferential regression plot diagnostics, and we discuss their strengths and limitations.

Investigating perceptions of patient-centred care in orthodontics.

OBJECTIVES: To assess and compare patient and clinician perceptions of patient-centredness for adults about to commence active orthodontic treatment, and to assess whether the following variables affected perceptions of patient-centredness: patient gender and age; clinician gender and grade; and stage of treatment. DESIGN: A prospective, cross-sectional questionnaire study. SETTING: Eastman Dental Hospital, UCLH NHS Foundation Trust. PARTICIPANTS: A total of 112 adult patients and 30 clinicians completed 224 questionnaires (112 patient and 112 clinician questionnaires). METHODS: A validated, dyadic questionnaire, the '9-Item Patient Perception of Patient-Centredness' (PPPC), was used to collect data from both patients and their corresponding clinicians after initial assessment or records/treatment planning consultations. Total PPPC scores (possible score range = 9-36) were calculated for each patient and clinician to ascertain the extent to which they perceived they were engaging in patient-centredness, where higher scores corresponded with better performance. RESULTS: Patients and clinicians perceived high engagement in patient-centredness with median scores of 32/36 and 29/36, respectively. There was a statistically significant difference between total scores with patients perceiving consultations to be more patient-centred than clinicians (P < 0.001). None of the variables (patient gender and age, clinician gender and grade, stage of treatment) were statistically significant. CONCLUSION: Patients and clinicians both perceived high engagement in patient-centredness. Patients perceived consultations to be significantly more patient-centred than clinicians (P < 0.001).

A cross-sectional study investigating patients' preferences regarding shared decision-making in adult orthodontic patients.

Introduction Shared decision-making (SDM) is a process by which patients and clinicians work collaboratively to make decisions about healthcare. Previously, research has shown that patients want to be more involved in decisions about their care, but more recent evidence suggests that some patients may not wish to have the level of involvement that is now expected of them.Aims This study therefore investigated adult orthodontic patients' preferred and perceived roles in treatment decision-making.Methods This cross-sectional study was carried out in a teaching hospital using the Control Preferences Scale (CPS). This involved asking patients to choose one of five cards relating, firstly, to their preferred role in orthodontic decision-making, and then their perceived role in their current orthodontic treatment decision-making, and these were compared.Results One hundred patients were recruited and perceived roles in decision-making tended to be more passive than those patients said they preferred. Males were significantly more likely to select a passive role than females (p = 0.018).Conclusions Adult orthodontic patients perceived a more passive role in their current treatment decisions than they would have preferred. This highlights the importance of clinicians asking patients about their preferred role in treatment decision-making from the outset.

Mendelian randomization incorporating uncertainty about pleiotropy.

Mendelian randomization (MR) requires strong assumptions about the genetic instruments, of which the most difficult to justify relate to pleiotropy. In a two-sample MR, different methods of analysis are available if we are able to assume, M1 : no pleiotropy (fixed effects meta-analysis), M2 : that there may be pleiotropy but that the average pleiotropic effect is zero (random effects meta-analysis), and M3 : that the average pleiotropic effect is nonzero (MR-Egger). In the latter 2 cases, we also require that the size of the pleiotropy is independent of the size of the effect on the exposure. Selecting one of these models without good reason would run the risk of misrepresenting the evidence for causality. The most conservative strategy would be to use M3 in all analyses as this makes the weakest assumptions, but such an analysis gives much less precise estimates and so should be avoided whenever stronger assumptions are credible. We consider the situation of a two-sample design when we are unsure which of these 3 pleiotropy models is appropriate. The analysis is placed within a Bayesian framework and Bayesian model averaging is used. We demonstrate that even large samples of the scale used in genome-wide meta-analysis may be insufficient to distinguish the pleiotropy models based on the data alone. Our simulations show that Bayesian model averaging provides a reasonable trade-off between bias and precision. Bayesian model averaging is recommended whenever there is uncertainty about the nature of the pleiotropy.

DataSHIELD: taking the analysis to the data, not the data to the analysis.

BACKGROUND: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK's proposed 'care.data' initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. METHODS: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC. RESULTS: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach. CONCLUSIONS: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property-the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.

Maximum likelihood pedigree reconstruction using integer linear programming.

Large population biobanks of unrelated individuals have been highly successful in detecting common genetic variants affecting diseases of public health concern. However, they lack the statistical power to detect more modest gene-gene and gene-environment interaction effects or the effects of rare variants for which related individuals are ideally required. In reality, most large population studies will undoubtedly contain sets of undeclared relatives, or pedigrees. Although a crude measure of relatedness might sometimes suffice, having a good estimate of the true pedigree would be much more informative if this could be obtained efficiently. Relatives are more likely to share longer haplotypes around disease susceptibility loci and are hence biologically more informative for rare variants than unrelated cases and controls. Distant relatives are arguably more useful for detecting variants with small effects because they are less likely to share masking environmental effects. Moreover, the identification of relatives enables appropriate adjustments of statistical analyses that typically assume unrelatedness. We propose to exploit an integer linear programming optimisation approach to pedigree learning, which is adapted to find valid pedigrees by imposing appropriate constraints. Our method is not restricted to small pedigrees and is guaranteed to return a maximum likelihood pedigree. With additional constraints, we can also search for multiple high-probability pedigrees and thus account for the inherent uncertainty in any particular pedigree reconstruction. The true pedigree is found very quickly by comparison with other methods when all individuals are observed. Extensions to more complex problems seem feasible.