ORCID Profile
0000-0001-7169-4087
Current Organisation
The University of Edinburgh
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Publisher: Cold Spring Harbor Laboratory
Date: 18-08-2022
DOI: 10.1101/2022.08.17.22278893
Abstract: Sotrovimab is a neutralising monoclonal antibody (nMAB), currently administrated in England to treat extremely clinically vulnerable COVID-19 patients. Trials have shown it to have mild or moderate side effects, however safety in real-world settings has not been yet evaluated. We used national databases to investigate its uptake and safety in community patients across England. We used a cohort study to describe uptake and a self-controlled case series design to evaluate the risks of 49 pre-specified suspected adverse events in the 2-28 days post-treatment. Between December 11, 2021 and May 24, 2022, there were 172,860 COVID-19 patients eligible for treatment. Of the 22,815 people who received Sotrovimab, 21,487 (94.2%) had a positive SARS-CoV-2 test and 5,999 (26.3%) were not on the eligible list. Between treated and untreated eligible in iduals, the mean age (54.6, SD: 16.1 vs 54.1, SD: 18.3) and sex distribution (women: 60.9% vs 58.1% men: 38.9% vs 41.1%) were similar. There were marked variations in uptake between ethnic groups, which was higher amongst Indian (15.0% 95%CI 13.8, 16.3), Other Asian (13.7% 95%CI 11.9, 15.8), White (13.4% 95%CI 13.3, 13.6), and Bangladeshi (11.4% 95%CI 8.8, 14.6) and lower amongst Black Caribbean in iduals (6.4% 95%CI 5.4, 7.5) and Black Africans (4.7% 95%CI 4.1, 5.4). We found no increased risk of any of the suspected adverse events in the overall period of 2-28 days post-treatment, but an increased risk of rheumatoid arthritis (IRR 3.08, 95% CI 1.44, 6.58) and of systematic lupus erythematosus (IRR 5.15, 95% CI 1.60, 16.60) in the 2-3 days post-treatment, when we narrowed the risk period. National Institute of Health Research (Grant reference 135561)
Publisher: Elsevier BV
Date: 07-2021
DOI: 10.1016/J.JAIP.2021.02.052
Abstract: The impact of hormone replacement therapy (HRT) on clinical outcomes in menopausal women is uncertain. To investigate the association between use of HRT and severe asthma exacerbation in perimenopausal and postmenopausal women with asthma. We used the Optimum Patient Care Research Database, a population-based longitudinal primary care database in the United Kingdom, to construct a 17-year (January 1, 2000, to December 31, 2016) cohort of perimenopausal and postmenopausal (46-70 years, N = 31,656) women. We defined use of HRT, its subtypes, and duration of HRT use. Severe asthma exacerbation was defined as an asthma-related hospitalization, emergency department visits due to asthma, and/or prescription of oral corticosteroids. Analyses were undertaken using multilevel mixed-effects Poisson regression. At baseline, 22% of women were using any HRT, 11% combined HRT, and 11% estrogen-only HRT. Previous, but not current, use of any (incidence rate ratio [IRR]: 1.24, 95% confidence interval [CI]: 1.22-1.26), combined (IRR: 1.28, 95% CI: 1.25-1.31), and estrogen-only HRT (IRR: 1.18, 95% CI: 1.14-1.21), and longer duration (1-2 years: IRR: 1.16, 95% CI: 1.13-1.19 3-4 years: IRR: 1.43, 95% CI: 1.38-1.48 5+ years: IRR: 1.32, 95% CI: 1.28-1.36) of HRT use were associated with increased risk of severe asthma exacerbation compared with nonuse. The risk estimates were greater among lean women (body mass index [BMI] <25 kg/m Use of HRT and subtypes, particularly previous, but not current, use and use for more than 2 years, is associated with an increased risk of severe asthma exacerbation in perimenopausal ostmenopausal women with established asthma. Lean women and smokers are at greater risk than heavier women and nonsmokers, respectively.
Publisher: Springer Science and Business Media LLC
Date: 12-2020
DOI: 10.1186/S12874-020-01184-8
Abstract: Records of medication prescriptions can be used in conjunction with pharmacy dispensing records to investigate the incidence of adherence , which is defined as observing the treatment plans agreed between a patient and their clinician. Using prescribing records alone fails to identify primary non-adherence medications not being collected from the dispensary. Using dispensing records alone means that cases of conditions that resolve and/or treatments that are discontinued will be unaccounted for. While using a linked prescribing and dispensing dataset to measure medication non-adherence is optimal, this linkage is not routinely conducted. Furthermore, without a unique common event identifier, linkage between these two datasets is not straightforward. We undertook a secondary analysis of the Salford Lung Study dataset. A novel probabilistic record linkage methodology was developed matching asthma medication pharmacy dispensing records and primary care prescribing records, using semantic (meaning) and syntactic (structure) harmonization, domain knowledge integration, and natural language feature extraction. Cox survival analysis was conducted to assess factors associated with the time to medication dispensing after the prescription was written. Finally, we used a simplified record linkage algorithm in which only identical records were matched, for a naïve benchmarking to compare against the results of our proposed methodology. We matched 83% of pharmacy dispensing records to primary care prescribing records. Missing data were prevalent in the dispensing records which were not matched – approximately 60% for both medication strength and quantity. A naïve benchmarking approach, requiring perfect matching, identified one-quarter as many matching prescribing records as our methodology. Factors associated with delay (or failure) to collect the prescribed medication from a pharmacy included season, quantity of medication prescribed, previous dispensing history and class of medication. Our findings indicate that over 30% of prescriptions issued were not collected from a dispensary (primary non-adherence). We have developed a probabilistic record linkage methodology matching a large percentage of pharmacy dispensing records with primary care prescribing records for asthma medications. This will allow researchers to link datasets in order to extract information about asthma medication non-adherence.
Publisher: Research Square Platform LLC
Date: 23-05-2023
Publisher: Elsevier BV
Date: 2023
DOI: 10.2139/SSRN.4343760
Publisher: BMJ
Date: 07-2019
DOI: 10.1136/BMJOPEN-2018-028375
Abstract: Asthma is a long-term condition with rapid onset worsening of symptoms (‘attacks’) which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificity to avoid unnecessary prescribing of preventative medications that carry an associated risk of adverse events. We aim to create a risk score to predict asthma attacks in primary care using a statistical learning approach trained on routinely collected electronic health record data. We will employ machine-learning classifiers (naïve Bayes, support vector machines, and random forests) to create an asthma attack risk prediction model, using the Asthma Learning Health System (ALHS) study patient registry comprising 500 000 in iduals across 75 Scottish general practices, with linked longitudinal primary care prescribing records, primary care Read codes, accident and emergency records, hospital admissions and deaths. Models will be compared on a partition of the dataset reserved for validation, and the final model will be tested in both an unseen partition of the derivation dataset and an external dataset from the Seasonal Influenza Vaccination Effectiveness II (SIVE II) study. Permissions for the ALHS project were obtained from the South East Scotland Research Ethics Committee 02 [16/SS/0130] and the Public Benefit and Privacy Panel for Health and Social Care (1516–0489). Permissions for the SIVE II project were obtained from the Privacy Advisory Committee (National Services NHS Scotland) [68/14] and the National Research Ethics Committee West Midlands–Edgbaston [15/WM/0035]. The subsequent research paper will be submitted for publication to a peer-reviewed journal and code scripts used for all components of the data cleaning, compiling, and analysis will be made available in the open source GitHub website ( ollytibble ).
Publisher: Wiley
Date: 27-07-2020
DOI: 10.1111/BCP.14458
Publisher: Wiley
Date: 09-09-2017
DOI: 10.1111/ANS.14225
Abstract: Compared with other doctors, surgeons are at an increased risk of medicolegal events, including patient complaints and negligence claims. This retrospective study aimed to describe the frequency and nature of complaints involving surgeons compared with physicians. We assembled a national data set of complaints about surgeons and physicians lodged with medical regulators in Australia from 2011 to 2016. We classified the complaints into 19 issues across four domains: treatment and procedures, other performance, professional conduct and health. We assessed differences in complaint risk using incidence rate ratios (IRRs). Finally, we used a multivariate model to identify predictors of complaints among surgeons. The rate of complaints was 2.3 times higher for surgeons than physicians (112 compared with 48 complaints per 1000 practice years, P < 0.001). Two-fifths (41%) of the higher rate of complaints among surgeons was attributable to issues other than treatments and procedures, including fees (IRR = 2.68), substance use (IRR = 2.10), communication (IRR = 1.98) and interpersonal behaviour (IRR = 1.92). Male surgeons were at a higher risk of complaints, as were specialists in orthopaedics, plastic surgery and neurosurgery. Surgeons are more than twice as likely to attract complaints as their physician peers. This elevated risk arises partly from involvement in surgical procedures and treatments, but also reflects wider concerns about interpersonal skills, professional ethics and substance use. Improved understanding of these patterns may assist efforts to reduce harm and support safe practise.
Publisher: IEEE
Date: 11-07-2022
Publisher: JMIR Publications Inc.
Date: 28-05-2020
DOI: 10.2196/16452
Abstract: In the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging. This study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies. We searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process. Our literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75%) studies and continuous in 12 (19%), and the feature type was unclear in the remaining 4 (6%) studies. A total of 23 (37%) studies used hierarchical clustering with Ward linkage, and 22 (35%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original s le sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86%) explained the methods used to determine the number of clusters, 24 (38%) studies tested the quality of their cluster solution, and 11 (17%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification. This review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings.
Publisher: Cold Spring Harbor Laboratory
Date: 12-06-2023
DOI: 10.1101/2023.06.09.23291195
Abstract: Severe acute respiratory syndrome coronavirus 2 is constantly evolving. The clinical benefit of coronavirus disease 2019 (COVID-19) treatments against new circulating variants remains unclear. We sought to describe the real-world use of, and clinical outcomes associated with, early COVID-19 treatments among non-hospitalised patients with COVID-19 at highest risk of developing severe disease in Scotland. Retrospective cohort study of non-hospitalised patients diagnosed with COVID-19 from 1 December 2021 to 25 October 2022, using administrative health data managed by Public Health Scotland and National Records of Scotland. Patients included in the study were aged ≥18 years, met at least one of the National Health Service highest-risk conditions criteria for early COVID-19 treatment, and had received outpatient treatment with sotrovimab, nirmatrelvir/ritonavir or molnupiravir, or no early COVID-19 treatment. Index date was defined as the earliest of either COVID-19-positive diagnosis or early COVID-19 treatment during the study period. Baseline patient characteristics and acute clinical outcomes in the 28 days following the index date were reported. To protect patient confidentiality, values of ≤5 were suppressed. A total of 2548 patients were included (492: sotrovimab, 276: nirmatrelvir/ritonavir, 71: molnupiravir, and 1709 eligible highest-risk untreated). Patients aged ≥75 years accounted for 6.9% (n=34/492) of the sotrovimab-treated group, 21.0% (n=58/276) of those treated with nirmatrelvir/ritonavir, 16.9% (n=12/71) of those treated with molnupiravir and 13.2% (n=225/1709) of untreated patients. Advanced renal disease was reported for 6.7% (n=33/492) of sotrovimab-treated and 4.7% (n=81/1709) of untreated patients, and five or fewer patients in the nirmatrelvir/ritonavir and molnupiravir cohorts. A high proportion of treated patients did not have a highest-risk condition reported in the database (71.7% for sotrovimab [n=353/492], 85.1% for nirmatrelvir/ritonavir [n=235/276], 85.9% for molnupiravir [n=61/71]). Five or fewer patients in each treated cohort experienced COVID-19-related hospitalisations during the 28-day acute period. For untreated patients, the percentage of COVID-19-related hospitalisations was 3.0% (n=48/1622). All-cause hospitalisations were experienced by 5.3% (n=25/476) of sotrovimab-treated patients, 6.9% (n=12/175) of nirmatrelvir/ritonavir-treated patients and 13.3% (n=216/1622) of untreated patients. Five or fewer patients in the molnupiravir cohort experienced all-cause hospitalisation. There were no deaths within 28 days of index for patients in the treated cohorts. Mortality was 4.3% (n=70/1622) in untreated patients (18.6% [n=13/70] had COVID-19 as the primary cause). In our analyses of outcomes for sotrovimab-treated and untreated patients during BA.1, BA.2 and BA.5 predominance, COVID-19-related hospitalisation rates were consistent, with n≤5 for sotrovimab-treated patients in each period. Our findings indicate that sotrovimab was often used amongst patients who were aged years old and had advanced renal disease. Among patients who received early COVID-19 treatment, proportions of all-cause hospitalisation and death within 28 days of treatment were low.
Publisher: Wiley
Date: 27-09-2017
DOI: 10.1111/INM.12380
Abstract: Restrictive practices are used in response to conflict and aggression in psychiatric inpatient settings. Reducing such practices is the focus internationally of policy and legislative change, many initiatives, and a growing body of research. Safewards is a model and a set of 10 interventions designed to reduce conflict and containment in inpatient services. In the current study, we aimed to assess the impact of implementing Safewards on seclusion in Victorian inpatient mental health services in Australia. The study used a before-and-after design, with a comparison group matched for service type. Thirteen wards opted into a 12-week trial to implement Safewards and 1-year follow up. The comparison group was all other wards (n = 31) with seclusion facilities in the jurisdiction, matched to service type. Mandatorily-reported seclusion event data for all 44 wards over a 15-month period were analysed using negative binomial regression. Adherence to Safewards was measured via fidelity checklists at four time points: twice during the trial, post-trial, and at 1-year follow up. Seclusion rates were reduced by 36% in Safewards trial wards by the 12-month follow-up period (incidence rate ratios (IRR) = 0.64,) but in the comparison wards seclusion rates did not differ from baseline to post-trial (IRR = 1.17) or to follow-up period (IRR = 1.35). Fidelity analysis revealed a trajectory of increased use of Safewards interventions after the trial phase to follow up. The findings suggest that Safewards is appropriate for practice change in Victorian inpatient mental health services more broadly than adult acute wards, and is effective in reducing the use of seclusion.
Publisher: Frontiers Media SA
Date: 11-06-2019
Publisher: F1000 Research Ltd
Date: 03-05-2023
DOI: 10.12688/WELLCOMEOPENRES.19078.1
Abstract: Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-s ling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85–0.87 and 1.00, 95% CI 0.95–1.05 respectively. In the external validation dataset, which included 2,670 participants aged years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83–0.88, and calibration slope 1.22, 95% CI 1.09–1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.
Publisher: F1000 Research Ltd
Date: 07-09-2023
Publisher: Elsevier BV
Date: 2023
DOI: 10.2139/SSRN.4356763
Publisher: Swansea University
Date: 25-02-2020
Abstract: IntroductionMore than 30 million adults are released from incarceration globally each year. Many experience complex physical and mental health problems, and are at markedly increased risk of preventable mortality. Despite this, evidence regarding the global epidemiology of mortality following release from incarceration is insufficient to inform the development of targeted, evidence-based responses. Many previous studies have suffered from inadequate power and poor precision, and even large studies have limited capacity to disaggregate data by specific causes of death, sub-populations or time since release to answer questions of clinical and public health relevance. ObjectivesTo comprehensively document the incidence, timing, causes and risk factors for mortality in adults released from prison. MethodsWe created the Mortality After Release from Incarceration Consortium (MARIC), a multi-disciplinary collaboration representing 29 cohorts of adults who have experienced incarceration from 11 countries. Findings across cohorts will be analysed using a two-step, in idual participant data meta-analysis methodology. ResultsThe combined s le includes 1,337,993 in iduals (89% male), with 75,795 deaths recorded over 9,191,393 person-years of follow-up. ConclusionsThe consortium represents an important advancement in the field, bringing international attention to this problem. It will provide internationally relevant evidence to guide policymakers and clinicians in reducing preventable deaths in this marginalized population. Key wordsMortality incarceration prison release in idual participant data meta-analysis consortium cohort.
Publisher: Swansea University
Date: 08-11-2019
Abstract: Background In the UK, issued prescriptions are typically taken to pharmacies, where medications are prepared, recorded, and dispensed. Data Linkage between prescribing and pharmacy dispensing records is not routinely conducted at the in idual prescription level for clinical care in England and Wales, however it can be particularly useful for the study of pharmacoepidemiology. With no unique prescribing event identifiers between records, an algorithmic approach is required for this linkage. Aims To create a linkage system for primary care prescribed asthma controller medications and pharmacy dispensing records. Methods Free text labels were used to populate fields for data linkage, relating to medication strength, medication type (active ingredients allows matching of generic substitutions to named brands), doses per medication unit, prescribed units, and prescribed doses. Prescribing and dispensing records were merged using an inner (many to many) join generating a candidate link for every combination of records matching on unique patient identifier and medicine. A recursive algorithm was developed and applied, working backwards chronologically through dispensing records and finding the most appropriate match based on the time since prescribing and agreement between the medication description fields. Unmatched records were assessed for quality assurance, and the distribution of linkage strength for matches was examined. Results We developed a harmonisation algorithm in a dataset of over 3 million asthma controller medication prescription records, for which almost 3 in 4 were coded according to the number of units (predominantly inhalers). Incorporating the estimated number of doses prescribed/dispensed into our wider matching algorithm, we were able to find unique prescription records for almost 95% of our dispensing records. Conclusion Early findings demonstrate the accuracy of the developed algorithm linking prescribing and dispensing records. This algorithm can easily be generalised to other conditions.
Publisher: BMJ
Date: 23-11-2020
DOI: 10.1136/THORAXJNL-2020-215540
Abstract: Longitudinal studies investigating impact of exogenous sex steroids on clinical outcomes of asthma in women are lacking. We investigated the association between use of hormonal contraceptives and risk of severe asthma exacerbation in reproductive-age women with asthma. We used the Optimum Patient Care Research Database, a population-based, longitudinal, anonymised primary care database in the UK, to construct a 17-year (1 January 2000–31 December 2016) retrospective cohort of reproductive-age (16–45 years, n=83 084) women with asthma. Using Read codes, we defined use, subtypes and duration of use of hormonal contraceptives. Severe asthma exacerbation was defined according to recommendations of the European Respiratory Society/American Thoracic Society as asthma-related hospitalisation, accident and emergency department visits due to asthma and/or oral corticosteroid prescriptions. Analyses were done using multilevel mixed-effects Poisson regression with QR decomposition. The 17-year follow-up resulted in 456 803 person-years of follow-up time. At baseline, 34% of women were using any hormonal contraceptives, 25% combined (oestrogen rogestogen) and 9% progestogen-only contraceptives. Previous (incidence rate ratio (IRR) 0.94, 95% CI 0.92 to 0.97) and current (IRR 0.96, 95% CI 0.94 to 0.98) use of any, previous (IRR 0.92, 95% CI 0.87 to 0.97) and current use of combined (IRR 0.93, 95% CI 0.91 to 0.96) and longer duration of use (3–4 years: IRR 0.94, 95% CI 0.92 to 0.97 5+ years: IRR 0.91, 95% CI 0.89 to 0.93) of hormonal contraceptives, but not progestogen-only contraceptives, were associated with reduced risk of severe asthma exacerbation compared with non-use. Use of hormonal contraceptives may reduce the risk of severe asthma exacerbation in reproductive-age women. Mechanistic studies investigating the biological basis for the influence of hormonal contraceptives on clinical outcomes of asthma in women are required. European Union electronic Register of Post-Authorisation Studies (EUPAS22967).
Publisher: BMJ
Date: 31-07-2023
Publisher: BMJ
Date: 12-2019
DOI: 10.1136/BMJOPEN-2019-030525
Abstract: To understand complaint risk among mental health practitioners compared with physical health practitioners. Retrospective cohort study, using incidence rate ratios (IRRs) to analyse complaint risk and a multivariate regression model to identify predictors of complaints. National study using complaints data from health regulators in Australia. All psychiatrists and psychologists (‘mental health practitioners’) and all physicians, optometrists, physiotherapists, osteopaths and chiropractors (‘physical health practitioners’) registered to practice in Australia between 2011 and 2016. Incidence rates, source and nature of complaints to regulators. In total, 7903 complaints were lodged with regulators over the 6-year period. Most complaints were lodged by patients and their families. Mental health practitioners had a complaint rate that was more than twice that of physical health practitioners (complaints per 1000 practice years: psychiatrists 119.1 vs physicians 48.0, p .001 psychologists 21.9 vs other allied health 7.5, p .001). Their risk of complaints was especially high in relation to reports, records, confidentiality, interpersonal behaviour, sexual boundary breaches and the mental health of the practitioner. Among mental health practitioners, male practitioners (psychiatrists IRR: 1.61, 95% CI 1.39 to 1.85 psychologists IRR: 1.85, 95% CI 1.65 to 2.07) and older practitioners (≥65 years compared with 36–45 years: psychiatrists IRR 2.37, 95% CI 1.95 to 2.89 psychologists IRR 1.78, 95% CI 1.47 to 2.14) were at increased risk of complaints. Mental health practitioners were more likely to be the subject of complaints than physical health practitioners. Areas of increased risk are related to professional ethics, communication skills and the health of mental health practitioners themselves. Further research could usefully explore whether addressing these risk factors through training, professional development and practitioner health initiatives may reduce the risk of complaints about mental health practitioners.
Publisher: Wiley
Date: 16-07-2018
DOI: 10.1111/ADJ.12625
Publisher: Springer Science and Business Media LLC
Date: 06-07-2018
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 26-11-2018
Publisher: JMIR Publications Inc.
Date: 30-09-2019
Abstract: n the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging. his study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies. e searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process. ur literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75%) studies and continuous in 12 (19%), and the feature type was unclear in the remaining 4 (6%) studies. A total of 23 (37%) studies used hierarchical clustering with Ward linkage, and 22 (35%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original s le sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86%) explained the methods used to determine the number of clusters, 24 (38%) studies tested the quality of their cluster solution, and 11 (17%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification. his review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings.
Publisher: SAGE Publications
Date: 10-06-2019
Abstract: This paper considers the impact of having a diagnosis of substance use disorder on the utilisation of compulsory orders under the Victorian Mental Health Act (2014). We analysed the subsequent treatment episodes over 2 years of people who had been on a community treatment order for at least 3 months and determined the odds of a further treatment order if there was a diagnosis of substance use at or about the time the index community treatment order ended. An additional diagnosis of a substance use disorder was coded in 47.7% and was associated with significantly increased odds of a subsequent treatment order in the following 2 years for those with a main diagnosis of schizophrenia (AOR = 3.03, p .001) and ‘other’ disorders (AOR = 11.60, p=0.002). Those with a main diagnosis of mood disorder had a significant increase in odds for an inpatient treatment order if there was an additional substance use disorder diagnosis (AOR = 3.81, p=0.006). Having an additional diagnosis of substance use disorder was associated with increased likelihood of being placed on an order. This study supports greater emphasis being given to treatment of substance use concurrently with that of mental illness.
Publisher: IEEE
Date: 10-2019
Publisher: Wiley
Date: 11-2021
DOI: 10.1002/CLT2.12075
Abstract: Mobile health interventions (MHI) offer the potential to help improve nasal corticosteroid (NCS) adherence in allergic rhinitis (AR). The aim of this systematic review was to summarise the current evidence on the effectiveness of MHI for improving NCS adherence in AR. We systematically searched MEDLINE, Embase and the Cochrane Central register of Controlled Trials (CENTRAL) for randomised controlled trials filtered for publication dates between 2010 and 2021. We evaluated the effects of MHI aiming to improve NCS adherence on self‐management outcomes in AR and comorbid conditions. Two reviewers independently screened potential studies, extracted study characteristics and outcomes from eligible papers and assessed risk of bias using the Cochrane Risk of Bias tool 2.0. High heterogeneity precluded meta‐analysis. Data were descriptively and narratively synthesised. Our searches identified 776 in idual studies of which 4 met the inclusion criteria. These studies were heterogeneous with respect to participant, intervention and outcome characteristics. We considered all outcome‐specific overall risk of bias assessments to be of high risk of bias except for two studies examining NCS adherence which received ‘some concern’ grades. The three studies which reported on NCS adherence found that MHI were associated with improvement in NCS adherence. Significant MHI‐associated improvement in symptoms or disease‐specific quality of life was found in one study each, whilst no study reported significant differences in nasal patency. Whilst MHI showed potential to improve NCS adherence, their effect on clinical outcomes varied. Furthermore, robust studies with longer intervention durations are needed to adequately assess effects of MHI and their in idual features on NCS adherence and clinical outcomes.
Publisher: SAGE Publications
Date: 21-03-2018
Abstract: To determine whether ‘older doctors’ (aged over 65) are at higher risk of notifications to the medical regulator than ‘younger doctors’ (aged 36–60 years) regarding their health, performance and/or conduct. Retrospective cohort study. National dataset of 12,878 notifications lodged with medical regulators in Australia between 1 January 2011 and 31 December 2014. All registered doctors in Australia aged 36–60 and years during the study period. Incidence rates of notifications and incidence rate ratios of notifications (older versus younger doctors). Older doctors had higher notification rates (90.9 compared with 66.6 per 1000 practitioner years, p 0.001). Sex-adjusted incidence rate ratios showed that older doctors had a higher risk of notifications relating to physical illness or cognitive decline (incidence rate ratio = 15.54), inadequate record keeping (incidence rate ratio = 1.98), unlawful use or supply of medications (incidence rate ratio = 2.26), substandard certificates/reports (incidence rate ratio = 2.02), inappropriate prescribing (incidence rate ratio = 1.99), disruptive behaviours (incidence rate ratio = 1.37) and substandard treatment (incidence rate ratio = 1.24). Older doctors had lower notification rates relating to mental illness and substance misuse (incidence rate ratio = 0.58) and for performance issues relating to problems with procedures (incidence rate ratio = 0.61). Older doctors were at higher risk for notifications relating to physical or cognitive impairment, records and reports, prescribing or supply of medicines, disruptive behaviour and treatment. They were at lower risk for notifications about mental illness or substance misuse. Incorporating knowledge of these patterns into regulatory practices, workplace adjustments and continuing education/assessment could enhance patient care.
Publisher: Ubiquity Press, Ltd.
Date: 31-08-2016
DOI: 10.5334/JORS.125
Publisher: Elsevier BV
Date: 2023
Publisher: MDPI AG
Date: 31-03-2017
Publisher: IEEE
Date: 09-12-2021
Publisher: MDPI AG
Date: 26-01-2018
Publisher: SAGE Publications
Date: 11-01-2021
Abstract: In Victoria, Prevention and Recovery Care Services have been established to provide a partial alternative to inpatient admissions through short-term residential mental health care in the community. This study set out to determine whether Prevention and Recovery Care Services are achieving their objectives in relation to reducing service use and costs, fostering least restrictive care and leading to positive clinical outcomes. We matched 621 consumers whose index admission in 2014 was to a Prevention and Recovery Care (‘PARCS consumers’) with 621 similar consumers whose index admission in the same year was to an acute inpatient unit and who had no Prevention and Recovery Care stays for the study period (‘inpatient-only consumers’). We used routinely collected data to compare them on a range of outcomes. Prevention and Recovery Care Services consumers made less subsequent use of acute inpatient services and, on balance, incurred costs that were similar to or lower than inpatient-only consumers. They were also less likely to spend time on an involuntary treatment order following their index admission. Prevention and Recovery Care Services consumers also experienced positive clinical outcomes over the course of their index admission, but the magnitude of this improvement was not as great as for inpatient-only consumers. This type of clinical improvement is important for Prevention and Recovery Care Services, but they may place greater emphasis on personal recovery as an outcome. Prevention and Recovery Care Services can provide an alternative, less restrictive care option for eligible consumers who might otherwise be admitted to an acute inpatient unit and do so at no greater cost.
Publisher: Oxford University Press (OUP)
Date: 19-10-2023
Publisher: Elsevier BV
Date: 08-2020
Publisher: SAGE Publications
Date: 18-11-2018
Abstract: Victoria, Australia, introduced reformed mental health legislation in 2014. The Act was based on a policy platform of recovery-oriented services, supported decision-making and minimisation of the use and duration of compulsory orders. This paper compares service utilisation and legal status after being on a community treatment order under the Mental Health Act 1986 (Vic) with that under the Mental Health Act 2014 (Vic). We obtained two distinct data sets of persons who had been on a community treatment order for at least 3 months and their subsequent treatment episodes over 2 years under the Mental Health Act and/or as an inpatient for the periods 2008–2010 (Mental Health Act 1986) and 2014–2016 (Mental Health Act 2014). The two sets were compared to assess the difference in use, duration and odds of having a further admission over 2 years. We also considered the mode of discharge – whether by the treating psychiatrist, external body or through expiry. Compared with the Mental Health Act 1986, under the Mental Health Act 2014, index community treatment orders were shorter (mean 227 days compared with 335 days) there was a reduction in the mean number of community treatment orders in the 2 years following the index discharge − 1.1 compared with 1.5 (incidence rate ratio (IRR) = 0.71, 95% confidence interval = [0.63, 0.80]) – and a 51% reduction in days on an order over 2 years. There was a reduction in the number of subsequent orders for those whose order expired or was revoked by the psychiatrist under the Mental Health Act 2014 compared to those under the Mental Health Act 1986. The number of orders which were varied to an inpatient order by the authorised psychiatrist was notably greater under the Mental Health Act 2014. The reformed Mental Health Act has been successful in its intent to reduce the use and duration of compulsory orders in the community. The apparent increase in return to inpatient orders raises questions regarding the intensity and effectiveness of community treatment and context of service delivery.
Publisher: Elsevier BV
Date: 06-2021
Publisher: Swansea University
Date: 05-09-2018
Abstract: IntroductionYoung people who have contact with the youth justice system are distinguished by a high prevalence of complex, co-occurring health problems, including known risk factors for preventable mortality. However, almost nothing is known about health outcomes for these young people after separation from the youth justice system. Objectives and ApproachWe aimed to examine the incidence, timing, causes and risk factors for death in justice-involved young people. We linked youth justice records in Queensland, Australia 1993-2016 (N=48,963) with adult correctional records and the National Death Index. We split the cohort into three subgroups: those who had ever been in detention (n=7,643), those supervised in the community but never detained (n=12,953), and those charged with an offence but never convicted (n=28,367). We calculated all-cause and cause-specific crude mortality rates (CMRs), and indirectly standardised mortality ratios (SMRs). We used Cox regression to identify static and time-varying risk factors for death. ResultsDuring a median of 13.6 years of follow-up there were 1,452 deaths (3.0%). The all-cause CMR was 2.2 (95%CI 2.1-2.3) per 1000 person-years, and the all-cause SMR was 3.1 (95%CI 3.0-3.3). The leading external causes of death were suicide (32% of all deaths), transport accidents (16%), accidental drug-related causes (13%), and violence (3%). In adjusted analyses, independent risk factors for all-cause mortality included being male (HR=1.4, 95%CI 1.2-1.6) and older ( =15 vs. vs. charge only HR=1.6, 95%CI 1.2-2.0) and subsequent incarceration as an adult (HR=1.8, 95%CI 1.4-2.4). Conclusion/ImplicationsYoung people who have contact with the youth justice system are at markedly increased risk of preventable death, after separation from that system. Efforts to improve long-term health outcomes for justice-involved youth have the potential to reduce preventable deaths in these highly vulnerable young people.
Publisher: Springer Science and Business Media LLC
Date: 03-11-2022
DOI: 10.1186/S12890-022-02189-3
Abstract: Asthma severity is typically assessed through a retrospective assessment of the treatment required to control symptoms and to prevent exacerbations. The joint British Thoracic Society and Scottish Intercollegiate Guidelines Network (BTS/SIGN) guidelines encourage a stepwise approach to pharmacotherapy, and as such, current treatment step can be considered as a severity categorisation proxy. Briefly, the steps for adults can be summarised as: no controller therapy (Step 0), low-strength Inhaled Corticosteroids (ICS Step 1), ICS plus Long-Acting Beta-2 Agonist (LABA Step 2), medium-dose ICS + LABA (Step 3), and finally either an increase in strength or additional therapies (Step 4). This study aimed to investigate how BTS/SIGN Steps can be estimated from across a large cohort using electronic prescription records, and to describe the incidence of each BTS/SIGN Step in a general population. There were 41,433,707 prescriptions, for 671,304 in iduals, in the Asthma Learning Health System Scottish cohort, between 1/2009 and 3/2017. Days on which an in idual had a prescription for at least one asthma controller (preventer) medication were labelled prescription events. A rule-based algorithm was developed for extracting the strength and volume of medication instructed to be taken daily from free-text data fields. Asthma treatment regimens were categorised by the combination of medications prescribed in the 120 days preceding any prescription event and categorised into BTS/SIGN treatment steps. Almost 4.5 million ALHS prescriptions were for asthma controllers. 26% of prescription events had no inhaled corticosteroid prescriptions in the preceding 120 days (Step 0), 16% were assigned to BTS/SIGN Step 1, 7% to Step 2, 21% to Step 3, and 30% to Step 4. The median days spent on a treatment step before a step-down in treatment was 297 days, whereas a step-up only took a median of 134 days. We developed a reproducible methodology enabling researchers to estimate BTS/SIGN asthma treatment steps in population health studies, providing valuable insights into population and patient-specific trajectories, towards improving the management of asthma.
Publisher: Elsevier BV
Date: 05-2021
Publisher: SAGE Publications
Date: 13-02-2020
Abstract: People released from prison are a socially marginalized group and are at high risk of death from preventable causes, including violence. Despite this, little is known about the epidemiology of violence-related death (VRD) after release from prison. This knowledge is essential for developing targeted, evidence-informed violence prevention strategies. We examined VRDs among a representative s le of people released from prisons in Queensland, Australia, by sex and Indigenous status. Correctional records for all people (aged ≥17 years) released from prisons from January 1994 until December 2007 ( N = 41,970) were linked probabilistically with the National Death Index. The primary outcome was VRD following release from prison. We calculated crude mortality rates (CMRs) and standardized mortality ratios (SMRs) standardized by age and sex to the Australian population. We used Cox regression to identify predictors of VRD. Of 2,158 deaths after release from prison, 3% ( n = 68) were violence-related. The SMR for VRD was 10.0 (95% confidence interval (CI): [7.9, 12.7]) and was greatest for women (SMR = 16.3, 95% CI: [8.2, 32.7]). The rate of VRD was 2.5 deaths per 10,000 person-years (95% CI: [2.0, 3.2]) and was highest between 2 and 6 months after release from prison (CMR = 6.3, 95% CI: [3.4, 11.6]). Risk factors for VRD included short sentences ( days for males and non-Indigenous people) and experiencing two or more imprisonments (for non-Indigenous people). No significant risk factors for VRD were identified for women or Indigenous people. People released from prison die from violence at a rate that is greatly elevated compared with the general population, with women experiencing the greatest elevation in risk. Reducing the number of VRDs in this population could improve the health and wellbeing of some of our most marginalized community members.
Publisher: BMJ
Date: 12-2022
Publisher: European Respiratory Society
Date: 03-2023
Publisher: Springer Science and Business Media LLC
Date: 14-09-2020
DOI: 10.1038/S41598-020-72060-0
Abstract: Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the ersity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-s le data).
Publisher: Springer Science and Business Media LLC
Date: 16-06-2020
DOI: 10.1186/S12913-020-05402-3
Abstract: There is an emerging international literature demonstrating clinical and cost-effectiveness of sub-acute residential mental health services. To date, however, there is limited information on the profile of consumers accessing these models of care. This study aimed to understand the profile of the population served by adult sub-acute residential mental health services in Victoria, Australia (known as Prevention and Recovery Care PARC) and to compare PARC service consumers with consumers admitted to psychiatric inpatient units within public hospitals. Using 5 years (2012–2016) of a state-wide database of routinely collected in idual level mental health service data, we describe the socio-demographic and clinical profile of PARC service consumers compared to consumers of psychiatric inpatient units including for primary diagnosis and illness severity. Using admissions as the unit of analysis, we identify the characteristics that distinguish PARC service admissions from psychiatric inpatient admissions. We also examine and compare length of stay for the different admission types. We analysed 78,264 admissions representing 34,906 in iduals. The profile of PARC service consumers differed from those admitted to inpatient units including for sex, age, diagnosis and illness severity. The odds of an admission being to a PARC service was associated with several socio-demographic and clinical characteristics. Being male or in the youngest age grouping ( 20 years) significantly reduced the odds of admission to PARC services. The presence of primary diagnoses of schizophrenia and related disorders, mood, anxiety or personality disorders, all significantly increased the odds of admission to PARC services. Predictors of length of stay were consistent across PARC and inpatient admission types. Our findings suggest PARC services may serve an overlapping but distinguishably different consumer group than inpatient psychiatric units. Future research on sub-acute mental health services should be cognizant of these consumer differences, particularly when assessing the long-term effectiveness of this service option.
Publisher: Springer Science and Business Media LLC
Date: 12-07-2023
Publisher: Research Square Platform LLC
Date: 29-11-2022
DOI: 10.21203/RS.3.RS-2033577/V1
Abstract: Background Medication adherence is usually defined as the extent of the agreement between the medication regimen agreed to by patients with their healthcare provider and the real-world implementation. Proactive identification of those with poor adherence may be useful to identify those with poor disease control and offers the opportunity for ameliorative action. Adherence can be estimated from Electronic Health Records (EHRs) by comparing medication dispensing records to the prescribed regimen. Several methods have been developed in the literature to infer adherence from EHRs, however there is no clear consensus on what should be considered the gold standard in each use case. Our objectives were to critically evaluate different measures of medication adherence in a large longitudinal Scottish EHR dataset. We used asthma, a chronic condition with high prevalence and high rates of non-adherence, as a case study. Methods Over 1.6 million asthma controllers were prescribed for our cohort of 91,334 in iduals, between January 2009 and March 2017. Eight adherence measures were calculated, and different approaches to estimating the amount of medication supply available at any time were compared. Results Estimates from different measures of adherence varied substantially. Three of the main drivers of the differences between adherence measures were the expected duration (if taken as in accordance with the dose directions), whether there was overlapping supply between prescriptions, and whether treatment had been discontinued. However, there are also wider, study-related, factors which are crucial to consider when comparing the adherence measures. Conclusions We evaluated the limitations of various medication adherence measures, and highlight key considerations about the underlying data, condition, and population to guide researchers choose appropriate adherence measures. This guidance will enable researchers to make more informed decisions about the methodology they employ, ensuring that adherence is captured in the most meaningful way for their particular application needs.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2021
End Date: 2022
Funder: Health Research Council of New Zealand
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